Neural Network

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.