What are tensor operations in machine learning?

What are tensor operations in machine learning?

Tensor operations are mathematical operations that involve tensors, which are multi-dimensional arrays. They are fundamental in machine learning, especially in deep learning, as they allow for efficient computation of data.

Can you give me some examples of tensor operations?

Sure! Common tensor operations include addition, multiplication, and contraction. For instance, you can add two tensors of the same shape or perform matrix multiplication on 2D tensors.

How do tensor operations differ from regular matrix operations?

Tensor operations generalize matrix operations. While matrices are 2D tensors, tensors can have more dimensions, allowing for more complex data representations and operations.

Why are tensor operations important in neural networks?

They are crucial because neural networks rely on tensors to represent inputs, weights, and outputs. Efficient tensor operations enable faster training and inference.