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Neural Network Architecture Visualizer

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Description

An interactive tool for building and visualizing different neural network architectures.

Learning Objectives

  • Explore how network architecture affects capacity and complexity
  • Visualize how neurons connect across layers in fully connected networks
  • Understand the role of depth (layers) and width (neurons per layer)
  • Observe forward propagation through the network layers

How to Use

  1. Adjust Layer Sizes: Use sliders to change neurons in hidden layers
  2. Select Presets: Choose common architectures (shallow, deep, wide)
  3. Animate: Click to see forward propagation in action
  4. Observe: Watch parameter count and connections update dynamically

Key Concepts

Network Depth

  • Number of layers in the network
  • Deeper networks learn hierarchical features
  • Each layer can transform representations

Network Width

  • Number of neurons per layer
  • Wider layers capture more features simultaneously
  • Trade-off: capacity vs overfitting risk

Parameters

  • Total learnable weights and biases
  • Formula: (inputs + 1) × outputs per layer
  • More parameters = more capacity but higher risk of overfitting

Architecture Types

  • Shallow: Few layers, may underfit complex patterns
  • Deep: Many layers, learns hierarchical representations
  • Wide: Many neurons per layer, high capacity per level

Interactive Features

  • Real-time Updates: See architecture change as you adjust sliders
  • Parameter Counter: Track total network parameters
  • Forward Propagation Animation: Visualize activation flow
  • Connection Visualization: Green = positive weights, red = negative weights