What is the role of tensors in neural networks?

What is the role of tensors in neural networks?

Tensors are multi-dimensional arrays that represent data in neural networks. They are essential for storing inputs, weights, and outputs.

Can you explain how tensors are used in training a neural network?

During training, tensors hold the input data and the model's parameters. They are manipulated through operations like addition and multiplication to compute gradients.

What types of tensors are commonly used?

Common types include scalars (0D), vectors (1D), matrices (2D), and higher-dimensional tensors. Each serves different purposes in computations.

How do tensors differ from regular arrays?

Tensors can have more than two dimensions and support operations that are optimized for deep learning, unlike regular arrays which are typically limited to 1D or 2D.

Are there specific libraries for working with tensors?

Yes, popular libraries like TensorFlow and PyTorch provide extensive support for tensor operations, making it easier to build and train neural networks.