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QDay Forum A.I A-Z Glossary

Started by RustyHawk, Feb 18, 2026, 12:01 AM

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Topic: QDay Forum A.I A-Z Glossary   Views(Read 24 times)

RustyHawk

AI GLOSSARY (A-Z)

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

A
Algorithm
A defined set of rules or steps a computer follows to solve a problem or perform a task.

B
Backpropagation
A training method for neural networks that adjusts weights by propagating errors backward through the network.

C
Convolutional Neural Network
A neural network architecture designed for processing grid-like data such as images.

D
Deep Learning
A subset of machine learning using multi-layered neural networks to model complex patterns.

E
Embedding
A numerical vector representation of data such as words or images that captures meaning or relationships.

F
Fine-Tuning
The process of taking a pre-trained model and adjusting it further on specific data.

G
Generative AI
AI systems that create new content such as text, images, or audio.

H
Hyperparameter
A configuration value set before training that controls how a model learns.

I
Inference
The process of using a trained model to make predictions or generate outputs.

J
Joint Probability
The probability of two or more events occurring at the same time.

K
Knowledge Graph
A structured representation of information showing relationships between entities.

L
Large Language Model
A neural network trained on vast amounts of text to understand and generate human language.

M
Machine Learning
A field of AI where systems learn patterns from data instead of being explicitly programmed.

N
Neural Network
A computational model inspired by the human brain, composed of interconnected nodes.

O
Overfitting
When a model learns training data too closely and performs poorly on new data.

P
Prompt Engineering
The practice of designing inputs to guide AI models toward desired outputs.

Q
Q-Learning
A reinforcement learning algorithm that learns optimal actions using reward feedback.

R
Reinforcement Learning
A learning method where agents improve by receiving rewards or penalties from actions.

S
Supervised Learning
Training a model using labeled data where correct outputs are known.

T
Transformer
A neural network architecture that uses attention mechanisms to process sequences efficiently.

U
Unsupervised Learning
Learning patterns from unlabeled data without predefined outputs.

V
Vector Database
A database optimized for storing and searching vector embeddings.

W
Weights
Parameters in a neural network that determine the strength of connections between nodes.

X
Explainable AI
Methods and techniques that make AI decisions understandable to humans.

Y
YOLO (You Only Look Once)
A real-time object detection algorithm that processes images in a single pass.

Z
Zero-Shot Learning
The ability of a model to perform tasks it was not explicitly trained on.

Inland Aidan

OMG thats great. And really needs to be stickied
I read every reply. Even the bad ones.

Inland Sienna

Still learning but that tracks. The more I read about this the more I realise how much I do not know.

Appreciate the detail.

SpinorWave

QuoteStill learning but that tracks. The more I read about this the more I realise how much I do not know. Appreciate the detail.

Same here. That is just how it is.

Ha, fair enough. :)

Ann

That checks out from what I have seen. Start there and see if it makes a difference.

Most AI tools I have tried are impressive for a session and then disappear from my routine.
RTFM and then ask

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