AI & Machine Learning Glossary
A
- Artificial Intelligence (AI)
- The simulation of human intelligence processes by computer systems, including learning, reasoning, and self-correction.
- Algorithm
- A set of rules or instructions given to an AI system to help it learn and make decisions.
D
- Deep Learning
- A subset of machine learning using neural networks with many layers to progressively extract higher-level features from raw input.
- Diffusion Model
- A type of generative model that creates images by gradually denoising random noise.
G
- GPT (Generative Pre-trained Transformer)
- A family of large language models developed by OpenAI that generate human-like text.
- GAN (Generative Adversarial Network)
- A ML framework where two neural networks contest to generate new, synthetic data.
L
- LLM (Large Language Model)
- AI models trained on massive text datasets to understand and generate human language. Examples: GPT-4, Claude, LLaMA.
M
- Machine Learning
- A subset of AI where systems learn from data to improve performance without explicit programming.
N
- Neural Network
- A computing system inspired by biological neural networks, consisting of interconnected nodes that process information.
- NLP (Natural Language Processing)
- The branch of AI focused on enabling computers to understand, interpret, and generate human language.
P
- Prompt Engineering
- The practice of designing inputs to get desired outputs from AI models.
R
- RAG (Retrieval-Augmented Generation)
- An AI approach combining retrieval of relevant documents with text generation.
- Reinforcement Learning
- A ML technique where agents learn by interacting with an environment and receiving rewards.
T
- Transformer
- A neural network architecture using attention mechanisms, forming the basis of models like GPT and BERT.
- Training Data
- The dataset used to teach a machine learning model to make predictions.