Tensor operations are a fundamental concept in machine learning and deep learning. In this tutorial, we'll explore some of the basic tensor operations and how they are used in AI models.

Basic Operations

Here are some common tensor operations:

  • Addition (+): Add two tensors element-wise.
  • Subtraction (-): Subtract two tensors element-wise.
  • Multiplication (*): Multiply two tensors element-wise.
  • Division (/): Divide two tensors element-wise.
  • Transpose (T): Permute the dimensions of a tensor.

Example

Suppose we have two tensors:

A = [[1, 2], [3, 4]]
B = [[5, 6], [7, 8]]

Here are the results of various operations:

  • Addition: A + B results in [[6, 8], [10, 12]].
  • Subtraction: A - B results in [-4, -4], [-1, -1]].
  • Multiplication: A * B results in [[5, 12], [21, 32]].
  • Division: A / B results in [0.2, 0.3333], [0.4286, 0.5]].
  • Transpose: A^T results in [[1, 3], [2, 4]].

Useful Links

For more in-depth information on tensor operations, check out our Advanced Tensor Operations Tutorial.

Images

Here are some examples of tensors:

Tensor Representation
Tensor Operations