Artificial Neural Networks Virtual Lab

Master the fundamentals of neural networks through interactive visualization

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⚡ Learn by Doing

Interactive network builder with challenges

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Neuron & Perceptron

Basic building block of neural networks

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Backpropagation

How networks learn from errors

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Network Architecture

Design multi-layer networks

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Gradient Descent

Optimization algorithms

Activation Functions

ReLU, Sigmoid, Tanh comparison

Interactive Network Builder - Learn by Doing!

Free Exploration Mode

Design your own neural network architecture! Add layers, adjust neurons, and watch it train.

Network Architecture

Input Layer

2 features

Hidden Layer 1

Output Layer

Training Settings

2
0.01

Network Stats

Total layers: 3

Hidden layers: 1

Total parameters: 27

Depth: 2

💡 Tips

  • More layers = deeper network
  • More neurons = more capacity
  • ReLU is popular for hidden layers
  • Sigmoid/Tanh for output layer