neural network
A neural network is a computational model composed of layered, interconnected units (neurons) that learn input-to-output mappings by adjusting weighted connections and applying nonlinear activation functions.
Training typically consists of a forward pass that produces predictions and a loss/objective, followed by gradient-based optimization: gradients are computed via backpropagation (automatic differentiation using the chain rule) and used to update parameters.
Architectures vary by data and task, including the following:
- Feedforward multilayer perceptrons
- Convolutional networks for images and other grid-structured data
- Recurrent networks for sequences
- Transformer-based models that use self-attention to capture long-range dependencies
- Graph neural networks for graph-structured data
Despite the biological inspiration, modern neural networks are mathematically defined, differentiable function approximators whose parameters (weights and biases) are learned from data by minimizing a loss under supervised, self-supervised, unsupervised, or reinforcement learning setups.
Related Resources
Course
Building a Neural Network & Making Predictions With Python AI
In this step-by-step course, you'll build a neural network from scratch as an introduction to the world of artificial intelligence (AI) in Python. You'll learn how to train your neural network and make accurate predictions based on a given dataset.
For additional information on related topics, take a look at the following resources:
By Leodanis Pozo Ramos • Updated July 29, 2026
