# Neural Network Basics
Confidence: high
Last verified: 2026-05-22
Generation: human_only


## TL;DR

Neural networks are computing systems inspired by biological neurons: layers of interconnected nodes that transform input data through weighted connections and activation functions. A simple feedforward network: Input Layer → Hidden Layer(s) → Output Layer. Each neuron computes weighted sum + bias + activation function.

## Core Explanation

Activation functions: ReLU (most common), sigmoid (binary out), tanh (-1 to 1), softmax (multi-class probability). Training: forward pass computes output, backward pass (backpropagation) updates weights via gradient descent. Universal Approximation Theorem: a single hidden layer can approximate any continuous function (given enough neurons).

## Further Reading

- [Deep Learning (Goodfellow, Bengio, Courville)](https://www.deeplearningbook.org/)