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Plain-language AI glossary

Term 68FundamentalsMeaning / context / connections

Fundamentals / Definition

Neural Network

A computational system inspired by the brain, composed of layers of interconnected nodes (neurons) that learn patterns from data through training.

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01

MeaningThe one-sentence definition.

02

ContextHow the idea works in practice.

03

UsesWhere the concept becomes useful.

01 / Plain-language context

How Neural Network works.

Neural networks are the foundation of all modern AI. At its simplest, a neural network is a function that takes input data, passes it through layers of mathematical transformations, and produces an output. Each layer applies weights (learned parameters), biases, and activation functions to transform the data. Training adjusts these weights to minimize prediction errors.

The key architectures: (1) Feedforward networks: data flows in one direction, used for simple classification and regression. (2) Convolutional Neural Networks (CNNs): specialized for images, using sliding filters to detect patterns. (3) Recurrent Neural Networks (RNNs/LSTMs): designed for sequences but largely replaced by transformers. (4) Transformers: the current dominant architecture, using attention mechanisms for parallel sequence processing. All modern LLMs are transformer-based neural networks.

For most AI application developers, neural network internals are abstracted away by APIs. You call OpenAI or Anthropic and get results. But understanding the basics helps you: reason about model behavior (why it makes certain errors), make informed decisions about model selection, and communicate effectively with ML engineers when needed.

02 / Practical uses

Where it helps.

  1. 01Image and object recognition
  2. 02Natural language processing
  3. 03Speech recognition
  4. 04Recommendation systems
  5. 05Anomaly detection
03 / Keep exploring
Transformer Architecture (Detailed)The complete technical architecture of the Transformer, including multi-head self-attention, positional encoding, feed-forward layers, and the encoder-decoder structure.Large Language Model (LLM)A neural network trained on massive text datasets that can generate, understand, and reason about human language.Computer VisionThe field of AI that enables machines to interpret and understand visual information from images and video.Foundation ModelA large, general-purpose AI model trained on broad data that serves as a base for many downstream tasks through fine-tuning, prompting, or adaptation.