Weight Initialization Methods in Neural Networks
Weight initialization is crucial in training neural networks, as it sets the starting point for optimization algorithms. The activation function applies a non-linear transformation in our network. Different activation functions serve different purposes. Choosing the right weight initialization and activation function is key to better neural network performance. Xavier initialization is ideal for Sigmoid or Tanh in feedforward networks. He initialization pairs well with ReLU for faster convergence, especially in CNNs. Matching these improves training efficiency and model performance.
Comparison of different initialization methods
