Neural Network Playground: Visualizing Training, Backprop & Decision Boundaries

What Neural Networks Are

Neural networks are computational models inspired by the structure and function of biological neurons. They consist of layers of interconnected nodes (neurons) that process information using weighted inputs, activation functions, and outputs.

These networks can learn to perform tasks such as classification, regression, and feature extraction through a training process where they adjust their weights based on input data.

The Training Process

Training a neural network involves presenting it with labeled data and adjusting the weights of its connections to minimize prediction errors. This is typically done using an algorithm called backpropagation, which calculates gradients for each weight by propagating error signals backward through the network.

During training, the network iteratively adjusts its parameters to improve performance on a given task, gradually converging towards optimal values.

Backpropagation and Gradient Descent

Backpropagation is a key algorithm used in training neural networks. It calculates the gradient of the loss function with respect to each weight by propagating error signals backward through the network, from output layer to input layer.

Gradient descent then uses these gradients to update the weights in the direction that minimizes the loss function, effectively guiding the network towards better predictions.

Decision Boundaries and Visualization

In a neural network with two or more classes, decision boundaries are the regions of input space where the output class changes. These boundaries can be visualized to understand how different neurons contribute to classification decisions.

By observing these boundaries, one can gain insights into the network's learning process and identify areas where the model may need further refinement.

Frequently asked questions

Try it live

Everything above runs in your browser — open Neural Network Playground — Visualise Training, Backprop & Decision Boundaries and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.