How do tensors work in machine learning?

Can you explain what tensors are in the context of machine learning?

Sure! Tensors are multi-dimensional arrays that generalize scalars, vectors, and matrices. They are fundamental in machine learning for representing data.

How do tensors differ from regular arrays?

Tensors can have more than two dimensions, while regular arrays are typically one or two-dimensional. This allows tensors to represent complex data structures.

Why are tensors important for deep learning?

Tensors are crucial in deep learning because they enable efficient computation and storage of data, especially when dealing with large datasets and neural networks.

Can you give an example of how tensors are used in a neural network?

Certainly! In a neural network, input data is often represented as tensors. For instance, an image can be a 3D tensor with dimensions for height, width, and color channels.