Instructions:
• 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
• Observe both pre-activation values (small labels) and post-activation values (in nodes)

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

Activation Functions:
• Linear: f(x) = x (no transformation)
• ReLU: f(x) = max(0, x) (clips negative values to 0)
• Sigmoid: f(x) = 1/(1 + e^(-x)) (outputs between 0 and 1)
• Tanh: f(x) = tanh(x) (outputs between -1 and 1)

Network Visualization:
• Node background intensity represents value magnitudes (brighter = larger values)
• Node colors distinguish positive (cooler hues) vs negative (warmer hues) values
• Node borders are green for positive, red for negative values
• Text color automatically adjusts for readability (white on dark backgrounds)
• Pre-activation labels are green for positive, red for negative values
• Connection thickness represents weight magnitudes
• Values display shows both pre-activation → post-activation for each neuron