Add interactive neural network visualization app with real-time training and architecture display

[Copilot]

Implements a Streamlit-based educational tool for visualizing neural network architecture, training dynamics, and gradient flow in real-time.

Core Implementation

Neural Network Engine (neural_network.py)

• Forward/backward propagation with gradient tracking
• 5 activation functions (ReLU, Sigmoid, Tanh, Leaky ReLU, Linear) with derivatives
• Xavier/He weight initialization
• Training history and state export for visualization

Visualization Layer (visualization.py)

• Network architecture graphs with neuron activation overlays
• Weight matrix and activation heatmaps
• Loss curves and gradient flow plots
• Activation function curves with derivatives

UI (app.py)

• 5-tab interface: Architecture, Training, Visualization, Analysis, Learn More
• Step-by-step and batch training modes
• 4 network types (Perceptron, 2-layer, 3-layer, custom) + 5 datasets
• Custom input prediction with live network state display

Example Usage

# Configure and initialize network
network = NeuralNetwork(
    layer_sizes=[2, 4, 1],
    activation_functions=['relu', 'sigmoid'],
    learning_rate=0.01
)

# Train with visualization
for epoch in range(100):
    loss = network.train_step(X, y)
    # Get state for real-time visualization
    state = network.get_network_state()
    # Visualize weights, activations, gradients

Documentation

• README.md: User guide with feature overview
• INSTALLATION.md: Setup with dependency troubleshooting
• QUICK_REFERENCE.md: Common configurations and scenarios
• ARCHITECTURE.md: Technical implementation details
• SUMMARY.md: Complete feature list and statistics

Original prompt

Create an advanced interactive application:

Project Requirements: Interactive Neural Network Visualization App

1. The application should allow users to select the neural network type (e.g., perceptron, two-layer network, three-layer network).
2. It should provide options to choose from multiple activation functions for the neurons (like ReLU, Sigmoid, Tanh, etc.).
3. Users should be able to input their own variable values and parameters for the network.
4. Based on the selected configuration, the app should visually display how data flows from input to output.
5. It should include visualizations of the training process, including gradient descent and backpropagation steps.
6. The visualization should be advanced and interactive, allowing users to see neuron activations and weight updates in real-time.
7. The app should be user-friendly so that even those who are not experts in neural networks can understand the visual flow.
8. Ensure that the app can be extended or customized easily for additional network types or learning algorithms in the future.

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