Matrix Multiplication
Explore the role of matrix multiplication in neural networks and understand how it simplifies complex calculations. This lesson teaches you the basics of matrices, their structure, and the rules for multiplying matrices efficiently, which is critical for building and optimizing machine learning models.
We'll cover the following...
Why do we use matrices?
If we manually do the calculations for a two-layer network with just two nodes in each layer, that would be enough work. But imagine doing the same for a network with five layers and hundreds of nodes in each layer. Just writing out all the necessary calculations would be a huge task. These can include combinations of combining signals, multiplied by the right weights, and applying the sigmoid activation function for each node and each layer. Clearly, there are too many manual calculations.
How can matrices help? Well, they help us in two ways. First, they allow us to compress writing all those calculations into a very simple short form. The second benefit is that many computer programming languages ...
That’s all a matrix is, a table or a grid of numbers, just like the following example of a 2 x 3 matrix:
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