AI & Data Terms
Plain-language explanations of data and AI terminology.
96 terms
Activation Function
A nonlinear function applied to neuron outputs — ReLU, sigmoid, GELU — that lets networks learn complex patterns.
Deep Learning · 1 min read
AI Agent
An LLM-powered system that plans steps, uses tools, and acts toward a goal with minimal human intervention.
AI Agents · 2 min read
Artificial Intelligence (AI)
Systems that perform tasks that typically require human intelligence — reasoning, perception, language, and decision-making.
AI Foundations · 1 min read
Attention Mechanism
A way for models to focus on relevant parts of the input when producing each output — the core idea behind transformers.
Deep Learning · 1 min read
Autoencoder
A neural network trained to compress input into a latent code and reconstruct it — useful for representation learning and anomaly detection.
Deep Learning · 1 min read
Backpropagation
The algorithm that computes how each neural network weight contributed to the error so gradient descent can update them.
Deep Learning · 1 min read
Batch Normalization
A technique that normalizes layer inputs across a mini-batch to stabilize and often speed up training.
Deep Learning · 1 min read
Bias in AI
Systematic unfairness or skewed behavior in data and models that harms some groups or languages more than others.
AI Foundations · 1 min read
Bias–Variance Tradeoff
The tension between models that are too rigid (high bias) and models that are too sensitive to training noise (high variance).
Machine Learning · 1 min read
Big Data
Datasets so large or fast that they need distributed storage and processing beyond a single machine's comfort zone.
Data Science · 1 min read
Chain of Thought
Prompting or training models to reason step by step before answering — often improving multi-step accuracy.
Prompt Engineering · 1 min read
CI/CD
Continuous Integration and Continuous Delivery — automating build, test, and deployment every time code changes.
DevOps · 1 min read
Classification
Predicting a discrete category or label for an input — spam vs not spam, language ID, sentiment class.
Machine Learning · 1 min read
Cloud Computing
Renting compute, storage, and managed services over the internet instead of owning physical servers.
Cloud Computing · 1 min read
Clustering
Grouping similar data points together without predefined labels — a core unsupervised technique.
Machine Learning · 1 min read
Computer Vision
The field of AI that enables machines to interpret images and video — detect, classify, segment, and describe visual content.
AI Foundations · 1 min read
Context Window
The maximum amount of text (in tokens) an LLM can consider in a single request — prompt plus response.
Large Language Models · 2 min read
Convolutional Neural Network (CNN)
A neural architecture that slides filters over grid-like data — images, spectrograms — to detect local patterns.
Deep Learning · 1 min read
Cross-Validation
A method to estimate how well a model generalizes by training and testing on multiple splits of the data.
Machine Learning · 1 min read
Data Labeling
Annotating examples with the correct tags, transcripts, boxes, or answers so supervised models can learn.
Data Science · 1 min read
Data Lake
A central store for large amounts of raw data in native formats — files, logs, images — before heavy structuring.
Data Science · 1 min read
Data Pipeline
An automated flow that moves and transforms data from sources to storage, analytics, or model training.
Data Science · 1 min read
Data Quality
How accurate, complete, consistent, and timely your data is — the hidden limiter of every ML system.
Data Science · 1 min read
Data Warehouse
A structured analytical database optimized for querying cleaned, modeled business and product data.
Databases · 1 min read
Dataset
A structured collection of examples used to train, validate, or evaluate machine learning models.
Data Science · 1 min read
Deep Learning
Machine learning using neural networks with many layers that learn hierarchical representations from data.
Deep Learning · 1 min read
Diffusion Model
A generative model that learns to reverse a gradual noising process — the engine behind many modern image and audio generators.
Deep Learning · 1 min read
DNS
Domain Name System — translates human-readable domain names like goobolabs.so into IP addresses computers use.
Networking · 1 min read
Docker
A platform for packaging applications and dependencies into portable containers that run consistently anywhere.
DevOps · 2 min read
Dropout
A regularization technique that randomly disables neurons during training to reduce overfitting.
Deep Learning · 1 min read
Embedding Model
A model specialized in mapping text (or other inputs) to vectors optimized for similarity, retrieval, and clustering.
Large Language Models · 1 min read
Embeddings
Dense numerical vectors that represent text, images, or other data so similar items sit close together in vector space.
AI Foundations · 2 min read
Encryption
Encoding data so only parties with the correct key can read it — essential for privacy and security.
Cybersecurity · 1 min read
Ensemble Learning
Combining multiple models to get better predictions than any single model alone — bagging, boosting, stacking.
Machine Learning · 1 min read
ETL / ELT
Extract, Transform, Load (or Extract, Load, Transform) — patterns for moving data from sources into analytics or ML systems.
Data Science · 1 min read
Exploratory Data Analysis (EDA)
Investigating a dataset with summaries and plots to understand distributions, issues, and promising signals before modeling.
Data Science · 1 min read
Feature Engineering
Creating informative input variables from raw data so models can learn patterns more effectively.
Data Science · 1 min read
Feature Store
A system for defining, storing, and serving ML features consistently offline for training and online for inference.
Data Science · 1 min read
Few-shot Learning
Getting a model to perform a task from just a handful of examples — often provided in the prompt for LLMs.
Large Language Models · 1 min read
Fine-tuning
Adapting a pre-trained model to a specific task or domain by training further on a smaller, targeted dataset.
Machine Learning · 2 min read
Foundation Model
A large model trained on broad data that can be adapted to many downstream tasks — the base layer of modern AI systems.
AI Foundations · 1 min read
Function Calling
Letting an LLM request structured tool calls — APIs, calculators, databases — instead of only generating free text.
AI Agents · 1 min read
GAN
Generative Adversarial Network — a generator and discriminator compete so the generator learns to create realistic synthetic data.
Deep Learning · 1 min read
Generative AI
AI systems that create new content — text, images, audio, code — rather than only classifying or scoring inputs.
AI Foundations · 1 min read
Git
A version control system that tracks code changes, enables collaboration, and supports branching and rollback.
Programming · 1 min read
GPU
Graphics Processing Unit — parallel hardware that accelerates training and inference for neural networks.
AI Foundations · 1 min read
Gradient Descent
The workhorse optimization algorithm that updates model weights by following the slope of the loss downhill.
Machine Learning · 1 min read
GraphQL
A query language for APIs that lets clients request exactly the fields they need in a single request.
Backend Development · 1 min read
Grounding
Connecting model outputs to verifiable sources — documents, tools, or databases — so answers stay tethered to evidence.
Large Language Models · 1 min read
Hallucination
When an LLM generates confident, plausible-sounding text that is factually wrong or unsupported.
Large Language Models · 1 min read
HTTP
Hypertext Transfer Protocol — the foundation of how browsers and apps request and send data on the web.
Networking · 1 min read
Hyperparameter
Settings you choose before training — learning rate, batch size, layers — that are not learned from the data itself.
Machine Learning · 1 min read
Inference
Running a trained model on new data to produce predictions or generated output — the "using" phase of ML.
Machine Learning · 1 min read
JavaScript
The programming language of the web — runs in browsers and on servers (Node.js) for full-stack development.
Programming · 1 min read
Knowledge Distillation
Training a smaller student model to mimic a larger teacher — compressing capability into a cheaper model.
Machine Learning · 1 min read
Kubernetes
An orchestration platform that runs, scales, and heals containerized applications across clusters of machines.
DevOps · 1 min read
Large Language Model (LLM)
A Transformer trained on massive text corpora to predict and generate language — the engine behind ChatGPT-style assistants.
Large Language Models · 2 min read
LoRA
Low-Rank Adaptation — a parameter-efficient fine-tuning method that trains small adapter matrices instead of all model weights.
Large Language Models · 1 min read
Loss Function
A score that measures how wrong a model's predictions are — training tries to make this number smaller.
Machine Learning · 1 min read
LSTM
Long Short-Term Memory — a recurrent architecture with gates that better remember long-range information than vanilla RNNs.
Deep Learning · 1 min read
Machine Learning
Systems that improve performance on a task by learning patterns from examples instead of explicit rules.
Machine Learning · 2 min read
Microservices
An architecture where an application is split into small, independently deployable services that communicate over the network.
Backend Development · 1 min read
MLOps
Practices and tools for deploying, monitoring, and iterating on ML models reliably in production — DevOps for machine learning.
Machine Learning · 1 min read
Model Evaluation
Measuring how well a model performs — metrics, benchmarks, human preference tests, and real-world online evaluation.
Machine Learning · 1 min read
Multimodal AI
Models that understand or generate across multiple modalities — text, images, audio, video — in one system.
AI Foundations · 1 min read
Natural Language Processing (NLP)
The field of AI focused on understanding, generating, and transforming human language with computers.
AI Foundations · 1 min read
Neural Network
A computing system of layered nodes that learns patterns by adjusting connection strengths through training.
Deep Learning · 1 min read
Normalization & Standardization
Rescaling numeric features so different units and ranges do not dominate training — min-max, z-score, and related methods.
Data Science · 1 min read
OAuth
An authorization standard that lets apps access user data on another service without sharing passwords.
Cybersecurity · 1 min read
Overfitting
When a model memorizes training data noise instead of learning patterns that generalize to new examples.
Machine Learning · 1 min read
Parquet
A columnar file format popular for analytics and ML datasets — efficient compression and fast column reads.
Data Science · 1 min read
Prompt Engineering
The practice of crafting instructions, examples, and context so LLMs produce reliable, useful outputs.
Prompt Engineering · 2 min read
Python
A readable, versatile programming language used everywhere from data science to web backends and AI tooling.
Programming · 2 min read
Quantization
Storing and computing model weights with fewer bits (e.g., 8-bit or 4-bit) to shrink memory use and often speed inference.
Large Language Models · 1 min read
Random Forest
An ensemble of decision trees trained on random subsets of data and features — robust and widely used on tabular data.
Machine Learning · 1 min read
Recurrent Neural Network (RNN)
A neural architecture that processes sequences step by step, carrying a hidden state through time — precursor to LSTMs and transformers.
Deep Learning · 1 min read
Regression
Predicting a continuous numeric value — price, temperature, score, or duration — from input features.
Machine Learning · 1 min read
Reinforcement Learning (RL)
Learning by trial and error — an agent takes actions, receives rewards, and improves a policy over time.
Machine Learning · 1 min read
REST API
A style of web API that uses HTTP methods and URLs to create, read, update, and delete resources.
Backend Development · 2 min read
Retrieval-Augmented Generation (RAG)
An architecture that retrieves relevant documents first, then asks an LLM to answer using that grounded context.
Large Language Models · 2 min read
RLHF
Reinforcement Learning from Human Feedback — aligning models with human preferences using ranked examples and a reward model.
Large Language Models · 1 min read
Semantic Search
Search that matches meaning and intent, not just exact keywords — powered by embeddings and similarity.
AI Foundations · 2 min read
Serverless
A cloud model where you run code in response to events without managing servers — billing per execution.
Cloud Computing · 1 min read
SQL
Structured Query Language — the standard way to store, query, and manage relational data in databases.
Databases · 1 min read
Streaming Data
Continuously generated events processed in near real time — logs, clicks, sensor readings — rather than only in daily batches.
Data Science · 1 min read
Supervised Learning
Training a model on labeled examples where each input is paired with the correct output.
Machine Learning · 1 min read
System Prompt
Instructions that set an LLM's role, rules, and behavior for a session — usually higher priority than user messages.
Prompt Engineering · 1 min read
Temperature
A sampling parameter that controls how random or focused an LLM's next-token choices are.
Large Language Models · 1 min read
Tokenization
Splitting text into subword units (tokens) that language models read, process, and generate.
Large Language Models · 2 min read
Train / Validation / Test Splits
Separating data so you train on one set, tune on another, and report honest performance on a final untouched test set.
Machine Learning · 1 min read
Transfer Learning
Reusing knowledge from a model trained on one task or dataset to improve performance on a related task with less data.
Deep Learning · 1 min read
Transformers
A neural network architecture that uses attention to model relationships between all parts of a sequence at once.
Deep Learning · 2 min read
Underfitting
When a model is too simple to capture the real patterns in the data — poor performance on both train and test.
Machine Learning · 1 min read
Unsupervised Learning
Finding structure in data without labeled answers — clustering, dimensionality reduction, and pattern discovery.
Machine Learning · 1 min read
Vector Database
A database optimized for storing embedding vectors and retrieving nearest neighbors by similarity at scale.
Databases · 2 min read
Zero-shot Learning
Performing a task with no task-specific examples — only instructions or class descriptions.
Large Language Models · 1 min read
Frequently asked
What is this glossary?
Plain-language explanations of AI and data terms — from tokenization to RAG — written for people without a computer science background.
Are the terms ranked by difficulty?
Yes — each entry is tagged Beginner, Intermediate, or Advanced, with related terms linked so you can follow a topic from fundamentals to depth.
Is it available in Somali?
Yes — the glossary itself is translated, not just the site navigation. Switch languages from the header to read it in Somali.