Neural Networks Learning Path

Artificial neuron, weights, and activation functions

An artificial neuron (node) is a simplified computational unit inspired by biological neurons. It receives real-valued signals from other neurons (or input data), combines them using connection strengths called weights, and then produces an output by applying a nonlinear transformation known as an activation function. Neurons are organized into layers (input, hidden, output), where each layer transforms its inputs and passes signals forward through the network. Weights determine how strongly each input contributes to a neuron’s output, and they are adjusted during training to reduce prediction error. The activation function introduces nonlinearity, enabling neural networks to learn complex patterns rather than only linear relationships. Different activation functions (e.g., historically including ReLU) affect how information flows through the network and how effectively training can optimize the model.

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Neural network layers and deep networks

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