Weight Initialization

Fact

A way to set model parameters before training starts.

In Plain Words

Weight initialization is like starting a bike in the right gear. Too high means wobbling. Too low means hamster pedaling.

It sets the model’s first numbers. It affects training speed and stability in deep networks.

Related Concepts

• Parameter
• Gradient Descent
• Backpropagation
• Neural Network

Parameter
Weight initialization sets starting values for parameters before training.

Gradient Descent
Weight initialization gives Gradient Descent its starting point.

Backpropagation
Good starting values help Backpropagation keep gradients stable.

Neural-network
A neural network needs weight initialization before training starts.