Glossary

0-9

0-shot learning 1-shot learning 2-stage detector 3D convolution 4D data 5G + AI 6DoF pose estimation 7D representation 8-bit quantization 9-layer network

A

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

B

Backpropagation Batch Normalization BERT Bias Boosting

C

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

D

Data Augmentation Deep Learning Deepfake Deterministic Model Discriminative Model

E

Embedding Encoder Ensemble Learning Epoch Explainable AI (XAI)

F

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

G

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

H

Hallucination Heuristic Hidden Layer Hierarchical Model Hyperparameter

I

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

J

JAX Jittering Joint Embedding JSONL / JSON-lines Juxtaposition

K

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

L

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

M

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

N

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

O

Objective Function One-hot Encoding Online Learning Optimizer Overfitting

P

Parameter Policy / Reinforcement Learning Policy Pooling Pretraining Prompt

Q

Q-learning Quality Estimation Quantization Query Queue / Buffer

R

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

S

Sampling Self-Supervised Learning Sequence Modeling Softmax Supervised Learning

T

Tokenizer Training Data Transfer Learning Transformer Tuning / Hyperparameter Tuning

U

U-Net Uncertainty Estimation Underfitting Universal Approximation Theorem Unsupervised Learning

V

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

W

Weak Supervision Weight Decay Whitening / Whitening Transformation Word Embedding Workflow

X

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

Y

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

Z

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

What is One-hot Encoding

One-hot Encoding is a widely used feature representation method primarily employed to convert categorical data into a format understandable by computers. In machine learning and data mining, effective representation of data is crucial for the success of models. The basic idea of One-hot Encoding is to transform each categorical value into a binary vector, where the position corresponding to the category is marked with a 1, and all other positions are marked with a 0.

The advantage of this method lies in its ability to eliminate ordinal relationships between categories, allowing models to treat each category independently. For instance, consider a dataset containing categories of animals such as “cat,” “dog,” and “bird.” Through One-hot Encoding, these categories can be represented as a three-dimensional array: [1, 0, 0], [0, 1, 0], and [0, 0, 1]. This representation helps enhance the learning effectiveness of models, especially in deep learning scenarios.

Despite its advantages, One-hot Encoding also has drawbacks. For example, when the number of categories is large, it can lead to the generation of sparse matrices, increasing computational complexity and memory usage. Furthermore, One-hot Encoding does not capture relationships between categories, which can potentially affect model performance. To address these issues, researchers have proposed alternative methods such as Target Encoding and Word Embedding.

Future trends involve combining One-hot Encoding with other encoding methods to reduce computational resource consumption and model complexity while maintaining effectiveness. Overall, One-hot Encoding is a fundamental technique in machine learning for handling categorical data, and understanding its principles and application scenarios is crucial for data scientists.

What is One-hot Encoding - Glossary