How to Use This Demo:
• Adjust the input values using the sliders or enter custom values
• Click on any node to view and modify its incoming weights
• Use Add Layer and Remove Layer buttons to change network architecture
• Change the number of units in each hidden layer using the controls
• Select different activation functions for each layer to see their effects
• Watch how values propagate through the network in real-time
• Use "Generate Random Inputs" to test with different input combinations

Interactive Features:
• Weight editing: Click any neuron to modify its incoming weights
• Architecture modification: Add/remove layers dynamically
• Layer customization: Change neuron counts and activation functions

Layer Types:
• Input Layer: 3 input values that you control
• Hidden Layers: Transform inputs using weights and activation functions
• Output Layer: Single output value representing the network's prediction

Visual Elements:
• Node borders are green for positive, red for negative values
• Connection color represents weight magnitudes

Architecture Guidelines:
• Start with 1-2 hidden layers for most problems
• Use 10-100 neurons per layer as a starting point
• Deeper networks can model more complex relationships but may be harder to train
• Width vs depth trade-offs: wider layers vs more layers

Activation Function Selection:
• ReLU is most common for hidden layers (fast computation, avoids vanishing gradients)
• Sigmoid for binary classification output layers
• Linear for regression output layers
• Tanh sometimes better than sigmoid in hidden layers (zero-centered)

Practical Considerations:
• Observe how different architectures affect the output for the same inputs
• Notice how activation functions shape the transformation at each layer
• Experiment with weight values to understand their impact
• Real networks require training data and optimization algorithms