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 rather than 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 entries are translated as well as the site navigation. Switch languages from the header to read it in Somali.