seniorDeep Learning
What is Weight Initialization in Deep Learning and why does it matter?
Updated May 16, 2026
Short answer
Weight initialization defines the starting values of neural network parameters and is critical for stable gradient flow and efficient training.
Deep explanation
Neural networks require carefully chosen initial weights to ensure stable training dynamics.
Why initialization matters:
- Poor initialization leads to vanishing or exploding gradients.
- Good initialization ensures stable signal propagation.
Key methods:
- Zero Initialization (bad):
- All weights become identical.
- Symmetry problem.
- Random Initialization:
- Breaks symmetry.
- Xavier Initialization:
- Designed for tanh/sigmoid activations.
- Keeps variance stable across layers.
- He Initialization:
- Designed for ReLU activations.
- Scales variance based on number of inputs.…
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