What are the different initialization techniques in machine learning?

What are the different initialization techniques in machine learning?

There are several initialization techniques, including random initialization, Xavier initialization, and He initialization. Each has its own advantages.

Can you explain how Xavier initialization works?

Xavier initialization sets the weights to values drawn from a distribution with a mean of 0 and a variance of 1/n, where n is the number of input neurons. This helps in maintaining the variance of activations across layers.

What about He initialization? How is it different?

He initialization is similar but is designed for layers with ReLU activation functions. It uses a variance of 2/n, which helps prevent dead neurons.

Which technique is generally preferred?

It depends on the activation function used. For ReLU, He initialization is often preferred, while Xavier is better for sigmoid or tanh activations.