The article explains linear algebra as a foundational mathematical concept for understanding functions and their transformations. It illustrates how multiple functions can be represented using matrices, which enable the projection of input vectors into different spaces. The transformation through matrix multiplication is integral to the concept of linear mappings. This discussion leads to the application of linear algebra in artificial neural networks (ANN), where input data, such as pixel values in images, is transformed into output data, revealing its essential role in machine learning.
Linear algebra simplifies understanding functions by using matrices, which represent sets of functions transforming input vectors into output vectors, crucial for artificial neural networks.
Collection
[
|
...
]