What core component is not part of the AI framework supported by MindSpore?

The AI framework supported by MindSpore focuses on enabling developers to build deep learning models efficiently and effectively. Core components of this framework typically include tensor operations, data handling and formatting, and neural networking capabilities.

Tensor operations are fundamental to the functioning of machine learning algorithms, allowing for efficient computation on multi-dimensional data. Data formatting is essential for preparing data inputs into forms that models can process, ensuring that datasets are compatible with the algorithms being employed. Neural networking is central to the creation of models that learn from data, encompassing the structure and training of artificial neural networks.

Signal processing, while an important field within data analysis and artificial intelligence, is not a core component within the MindSpore AI framework. It generally refers to techniques for analyzing, modifying, and synthesizing signals, such as audio or visual data, but does not directly address the operations and methodologies primarily focused on by MindSpore, which centers around neural networks and tensor manipulations in the context of deep learning.