Tensor Properties

In the rapidly evolving landscape of AI and machine learning, tensors have emerged as a fundamental structure, bridging complex data representations with efficient computation. As multidimensional arrays, tensors extend beyond mere matrices or vectors, allowing for intricate data manipulations essential in neural networks, computer vision, and natural language processing.

This chapter dives into the major properties of tensors, unraveling their dimensions, ranks, shapes and types. By understanding these properties, you’ll gain insight into the mathematical backbone that powers many of today’s AI models.