What are multi-dimensional arrays and how are they used in data science?

What are multi-dimensional arrays and how are they used in data science?

Multi-dimensional arrays are data structures that can hold data in multiple dimensions, like 2D or 3D. In data science, they are often used to represent datasets, images, or any data that can be structured in a grid-like format.

Can you give me an example of a multi-dimensional array?

Sure! A common example is a 2D array representing a matrix, like a table of numbers. For instance, a 3x3 matrix could look like this: [[1, 2, 3], [4, 5, 6], [7, 8, 9]]. Each element can be accessed using two indices.

How do libraries like NumPy handle multi-dimensional arrays?

NumPy provides a powerful array object called ndarray, which allows for efficient storage and manipulation of multi-dimensional arrays. It supports various operations like slicing, reshaping, and mathematical computations.

What are some applications of multi-dimensional arrays in machine learning?

In machine learning, multi-dimensional arrays are used to represent datasets, features, and labels. They are essential for operations in neural networks, where inputs and outputs can be multi-dimensional.