Tensor computation is a fundamental concept in modern machine learning and deep learning. This tutorial will introduce you to the basics of tensor computation and its applications.

Basic Concepts

  • Tensor: A tensor is a generalization of vectors and matrices. It is a multi-dimensional array of numbers.
  • Elements: The individual numbers in a tensor are called elements.
  • Dimensions: The number of dimensions of a tensor is its rank.

Operations

  • Addition and Subtraction: Tensors can be added or subtracted element-wise.
  • Multiplication: Tensors can be multiplied in several ways, including dot product, matrix multiplication, and element-wise multiplication.
  • Division: Tensors can be divided element-wise.

Example

Let's consider a simple example of tensor multiplication:

import numpy as np

# Create two tensors
tensor1 = np.array([[1, 2], [3, 4]])
tensor2 = np.array([[5, 6], [7, 8]])

# Matrix multiplication
result = np.dot(tensor1, tensor2)
print(result)

Resources

For more in-depth learning, you can refer to the following resources:

Tensor