Neural Network Playground // Train Your Mind

Interactive visualization of how neural networks learn and make decisions

Network Architecture

Hidden Layers:

Neurons per Layer: 4

Learning Parameters

Learning Rate: 0.1

Activation Function:

Dataset

Training Controls

Training Progress

Epoch: 0

Loss: --

Accuracy: --

Dataset & Decision Boundary

Loss Over Time

Concepts

Forward Propagation

Data flows from input to output through weighted connections.

Backpropagation

Network learns by adjusting weights based on prediction errors.

Activation Functions

Introduce non-linearity to enable complex pattern learning.

Learning Rate

Controls how quickly the network adapts to new information.

How Neural Networks Learn

🧠

Neurons & Layers

Each neuron receives inputs, applies weights, and passes the result through an activation function. Multiple layers allow the network to learn complex patterns.

⚡

Forward Pass

Data flows from input layer through hidden layers to output layer. Each connection has a weight that determines influence.

🔄

Backpropagation

The network calculates errors and adjusts weights backwards through layers to minimize prediction mistakes.

📈

Gradient Descent

An optimization algorithm that finds the best weights by following the steepest descent of the loss function.