Why Xavier and He Initialization Decide Whether Your Neural Network Learns or Fails
6 min read22 hours ago
–
Your neural network is ready. Layers are stacked. Activation functions chosen. Optimization algorithm selected. You run the first epoch expecting progress. Instead, the loss plateaus or explodes. Gradients vanish. Convergence stalls. The network fails to learn meaningfully.
The culprit? Weight initialization.
It seems paradoxical: before training even begins, the initial values of weights — random numbers between -1 and 1 — determine whether your network learns efficiently or gets stu…
Why Xavier and He Initialization Decide Whether Your Neural Network Learns or Fails
6 min read22 hours ago
–
Your neural network is ready. Layers are stacked. Activation functions chosen. Optimization algorithm selected. You run the first epoch expecting progress. Instead, the loss plateaus or explodes. Gradients vanish. Convergence stalls. The network fails to learn meaningfully.
The culprit? Weight initialization.
It seems paradoxical: before training even begins, the initial values of weights — random numbers between -1 and 1 — determine whether your network learns efficiently or gets stuck. Yet this fundamental technique is often overlooked. Practitioners default to whatever initialization their framework provides. They don’t understand why.
This is a mistake. Understanding weight initialization — Xavier, He, and their variants — separates practitioners who build robust models from those who debug mysterious convergence failures. This comprehensive guide explores the mathematical foundations, practical implementations, and when to apply each technique.
Press enter or click to view image in full size