Random Initialization
Random Initialization is the process of assigning randomly generated initial values to the parameters of a machine learning model before training begins.
What is Random Initialization?
In neural networks, weights are typically initialized with small random values before training. This gives different neurons different starting points, allowing them to learn different features as the optimization process updates the model parameters.
Why is Random Initialization Important?
Proper initialization can help neural networks learn effectively and avoid problems such as identical neuron behavior, slow convergence, or unstable training. The initialization strategy can also affect the model's final performance and training efficiency.
Common use cases
Random Initialization is commonly used when training neural networks, deep learning models, and other machine learning algorithms that require parameters to be initialized before optimization.