XenonStack Glossary

A

• AI-Driven Automation: Meaning, Benefits, Uses

• Adam Optimizer Explained: How Adaptive Moments Work

• Automated Machine Learning (AutoML) Explained

• Apache Hadoop: HDFS, YARN and MapReduce Explained

• Apache Spark: Architecture, Components and Uses

• Augmented Intelligence: Definition and Use

• Augmented Data Quality: How AI Improves Data

B

• BERT Explained: Bidirectional Transformers

• Bootstrap Aggregation (Bagging): How It Cuts Variance

• Back-Propagation Algorithm: How Neural Nets Learn

• Bias Errors in Machine Learning: Bias vs Variance

• Bayesian Statistics: Priors, Posteriors, Uses

• Business Analytics: Types, Uses and BI Difference

C

• Concordant-Discordant Ratio: Kendall's Tau Explained

• Cross-Validation in Machine Learning: Methods Guide

• Concave and Convex Functions Explained

• Capsule Networks (CapsNet) vs CNN: A Plain Guide

• Composable Infrastructure: How It Works and Why

• Cloud Data Warehouse: Definition and Benefits

D

• Decision-Driven Data Analytics: A Practical Guide

• Deep Neural Networks: Layers, Training and Uses

• Dropout Regularization: Reduce Overfitting

• Data Mining: Process, Techniques and Applications

• Dopamine: Reinforcement Learning Framework Guide

• Deep Reinforcement Learning: How It Works

• Data Governance: Definition, Pillars and AI Agents

• Data Observability: Five Pillars and AI Agents

• Data Discovery: Definition, Process and Tools

E

• ELMo Explained: Embeddings from Language Models

• End-to-End Machine Learning: Meaning and Examples

• Explainable AI (XAI): Principles and Methods

• Edge Computing in Agriculture: Use Cases and Limits

• Evaluation Metrics for Machine Learning Models

• Exploratory Data Analysis (EDA): Methods and Steps

• ELI5 Python Library: Explain ML Models

• Edge Analytics: Definition, Benefits and Use Cases

• Edge Computing: Definition, Benefits and Edge AI

• Enterprise Metadata Management (EMM) Explained

• Enterprise Information Management (EIM) Explained

F

• Feature Hashing: The Hashing Trick Explained

• Fine-Tuning: Methods, LoRA and When to Use It

• Few-Shot Learning: Methods and N-Way K-Shot Guide

• Factor Analysis: Meaning, Methods and PCA Difference

G

• Gated Recurrent Unit (GRU): How It Works vs LSTM

• Goodness of Fit: Tests, Metrics and Model Fit

• Gradient Descent: How the Algorithm Works

• Gradient Boosting: How It Works and When to Use It

• Generative Adversarial Networks (GANs) Explained

H

• Hyperparameter Tuning: Grid and Random Search Guide

• Hybrid Learning Models: Deep Learning with Uncertainty

• How a BI Programme Works: Process and Roles

I

• Internet of Behaviors (IoB): Meaning and Uses

J

K

L

• LightGBM: Fast Gradient Boosting Framework Guide

• Lean and Augmented Data Learning: A Practical Guide

M

• Multimodal AI: How It Works and Where It Fits

• Meta-Learning: Learning to Learn Explained

N

O

P

• PyText: NLP Framework on PyTorch, Status and Uses

• Probabilistic Programming: Models, Tools and Uses

Q

• Quantum Computing: Qubits, Superposition and Uses

• Quantum Machine Learning: What It Is and Where It Stands

R

S

• Sustainable Technology: Meaning, Examples, Uses

• Stochastic Gradient Descent (SGD): How It Works

T

• Test Automation Services: Scope, Tools, Practices

• Technological Convergence: Meaning and Examples

U

• ULMFiT Explained: Universal Language Model Fine-Tuning

V

• Vowpal Wabbit: Fast Online Machine Learning

W

• What Is Digital Asset Management (DAM)?

• What Is Composite AI? Definition and Examples

• What Is Synthetic Data? Methods, Benefits, Risks

• What Are Intelligent Applications? Definition Guide

• What Is Model Compression? Methods Explained

• What Is Average Pooling in CNNs? Explained

• What Is Google Duplex? How the AI Phone Agent Works

• What Is Convolution? CNN Basics Explained

• What Is Business Intelligence? Definition and Parts

• What Is ETL? Extract, Transform, Load Explained

• What Is a Data Catalog? Definition and Key Parts

• What Is Data Lineage? Definition and OpenLineage

• What Is a Data Mesh? Four Principles Explained

X

Y

Z