Weight Initialization Strategies - Interactive Visualization

Poor weight initialization can make deep networks untrainable from the first forward pass. If weights are too small, activations shrink to zero through each layer (vanishing); if too large, they explode. Xavier initialization sets variance to 1/fan_in for tanh networks, while He initialization doubles that variance for ReLU networks to compensate for the half-zeroed outputs.

• See activation distributions across all 10 layers for each initialization scheme - from collapsed zeros to healthy Gaussians
• Understand why all-zeros initialization is fatal: every neuron computes the same thing and gradients are identical - symmetry never breaks
• Compare Xavier vs He initialization: He uses a 2x larger variance to account for ReLU zeroing half its inputs
• Watch the gradient magnitude at each layer - with bad init, gradients at layer 1 are 1000x smaller than at the output
• Learn orthogonal initialization: useful for RNNs where preserving gradient norms through time steps is critical

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