What is weight initialization in neural networks?

What is weight initialization in neural networks?

Weight initialization refers to the process of setting the initial values of the weights in a neural network before training.

Why is weight initialization important?

It's crucial because it can affect the convergence speed and the final performance of the model. Poor initialization can lead to slow training or getting stuck in local minima.

What are some common methods for weight initialization?

Common methods include zero initialization, random initialization, Xavier initialization, and He initialization, each suited for different activation functions.

How do I choose the right weight initialization method?

The choice depends on the architecture of your network and the activation functions used. For example, Xavier is often used for sigmoid or tanh, while He is better for ReLU.