Backpropagation in Neural Networks

Backpropagation is a core neural network training concept, introduced in the 1960s, used to adjust model weights by propagating error backward through the network. It is a foundational idea in machine learning and neural network optimization.

• Backpropagation is used in neural network training.
• It works by sending error information backward through the network.
• The concept was first introduced in the 1960s.
• It is a foundational method in machine learning optimization.

<aside> 💡 Backpropagation helps neural networks learn by correcting weights based on output error.

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Neural network structure and weights

An artificial neural network is composed of connected input and output units, with each connection carrying a weight. The idea is inspired by biological neural networks, where neurons are linked across layers.

• Networks consist of interconnected units (nodes/neurons).
• Connections between units have adjustable weights.
• Artificial networks are modeled after biological neural networks.
• This section is setting up the backpropagation explanation.

<aside> 💡 Understand the network’s weighted connections first, since backpropagation works by adjusting those weights.

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Backpropagation algorithm overview

Backpropagation is used to reduce the cost function and overall prediction error in a neural network. The example shown is a network with an input layer and two hidden layers, illustrating how data flows through the model.

• Backpropagation adjusts the network to lower cost and error.