What is the difference between a tensor and an array?

What is the difference between a tensor and an array?

A tensor is a mathematical object that generalizes scalars, vectors, and matrices, while an array is a data structure that can hold a collection of items. Tensors can have multiple dimensions, just like arrays, but they are often used in more complex mathematical operations.

Can you give me an example of a tensor?

Sure! A 2D tensor can be represented as a matrix, like a grid of numbers. For example, a 2x2 tensor could look like this: [[1, 2], [3, 4]].

How are tensors used in machine learning?

Tensors are fundamental in machine learning, especially in deep learning frameworks like TensorFlow and PyTorch. They are used to represent data inputs, weights, and outputs in neural networks.

Are arrays and tensors interchangeable?

Not exactly. While both can represent multi-dimensional data, tensors have additional properties and operations that make them more suitable for mathematical computations in fields like physics and machine learning.