Description: Parameter initialization is the process of setting the initial values of parameters before training a machine learning model. This step is crucial, as the chosen values can significantly influence the convergence and performance of the model. Proper initialization can help avoid issues such as getting stuck in local minima or divergence of the optimization algorithm. There are various strategies for parameter initialization, ranging from assigning random values within a specific range to using more sophisticated techniques like He initialization or Xavier initialization, which are designed to maintain the variance of activations across layers of a neural network. The choice of initialization technique may depend on the type of model and the activation function used. For example, in deep neural networks, poor initialization can lead to the upper layers not learning properly, resulting in suboptimal performance. Therefore, parameter initialization is not just a technical step but also a strategic aspect of machine learning model design that can determine the success or failure of training.
