Description: Tensor initialization in deep learning frameworks refers to the process of assigning initial values to tensors before starting the training of a machine learning model. Tensors are fundamental data structures that enable efficient calculations in mathematical operations and the construction of neural networks. The way these tensors are initialized can significantly influence the model’s performance, as poor initialization can lead to issues such as slow convergence or getting stuck in local minima. There are various initialization strategies, such as random initialization, Xavier initialization, and He initialization, each designed to address different types of networks and activation functions. Choosing the right initialization technique is crucial to ensure that the network weights start within a range that facilitates effective learning. Tensor initialization can be easily performed using built-in functions that allow creating tensors with specific values, whether zeros, ones, or random distributions. This flexibility and ease of use make tensor frameworks popular tools among researchers and developers in the field of deep learning.
