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 Classifier / Classification

The terms classifier and classification are pivotal in the fields of machine learning and data science. A classifier is an algorithm or model that assigns data samples to specific categories, while classification refers to the overall activity of this process. This task is crucial across various applications such as spam detection, image recognition, and sentiment analysis.

Classifiers typically learn features and patterns from training data to effectively classify new data when it is encountered. Common classification algorithms include decision trees, support vector machines (SVM), and neural networks. Each algorithm has its unique advantages and disadvantages, making them suitable for different types of data and tasks.

In the medical field, classifiers can assist doctors in categorizing patients into different diseases based on symptoms; in finance, they can identify potential fraudulent transactions. Additionally, social media platforms leverage classification algorithms to recommend content to users, thereby enhancing user engagement.

As artificial intelligence technologies continue to advance, the accuracy and efficiency of classifiers will significantly improve. In the future, the application of deep learning models will further propel the development of classification techniques, enabling them to handle more complex datasets and tasks.

The main advantage of classifiers is their ability to automate and streamline data processing. However, they also have drawbacks, including dependence on training data and potential overfitting. When selecting a classifier, users should consider the characteristics of the data, the complexity of the task, and the interpretability of the model.

When using classifiers, data preprocessing, feature selection, and model evaluation are crucial steps. Ensuring data quality and diversity will help enhance the performance and reliability of classification models.