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Neural Networks

Understand how artificial neural networks learn and make predictions. Visualize neurons, weights, activations, and the learning process.

Key Concepts

Neurons & Weights

Understand how artificial neurons process inputs with learned weights

Activation Functions

See how non-linear functions enable networks to learn complex patterns

Backpropagation

Watch gradients flow backward to update weights and minimize error

Network Architectures

Explore different layer configurations and their capabilities

Interactive Visualizations

Simple Perceptron

Beginner

Understand the building block of neural networks

WeightsBiasActivationLinear Classification

Backpropagation

Intermediate

See how neural networks learn through gradient descent

Chain RuleGradientsWeight UpdatesLearning

Activation Functions

BeginnerComing Soon

Compare different activation functions and their properties

SigmoidReLUTanhSoftmax

Multi-Layer Perceptron

IntermediateComing Soon

Build and train a complete feedforward neural network

Hidden LayersForward PassTrainingNon-linearity

Recommended Learning Path

  1. 1

    Start with Simple Perceptron

    Learn the basic building block and understand weights and bias

  2. 2

    Explore Activation Functions

    See how different functions affect network behavior

  3. 3

    Understand Backpropagation

    Watch how networks learn by propagating errors backward

  4. 4

    Build Multi-Layer Networks

    Combine concepts to create powerful deep networks