Neural networks can be understood as a series of transformations applied to tensors or matrices of real numbers.
The input to a neural network is typically a tensor (a multi-dimensional array) of real numbers, representing data such as images, text, or signals.
As this input passes through each layer of the network, it is multiplied by weight matrices and transformed by activation functions, producing new tensors at each step.
These layers progressively extract higher-level features from the data, refining the information until the final layer generates an output.
C: @3blue1brown
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