Why neural networks use activation functions.

Описание: Neural networks use activation functions to introduce non-linearity into the model, which is a requirement for capturing relationships that are not just a line.

Without activation functions, each neuron would perform only a linear transformation of its input, meaning the entire network, no matter how deep, would collapse into a simple linear model.

It would not be possible to accurately approximate complex, non-linear functions. Activation functions such as the ReLU, sigmoid, or tanh enable neurons to respond in non-linear ways, allowing the network to bend and shape its decision boundaries to better fit the target function.

By stacking layers with these nonlinear activations, neural networks can learn the input-output mappings, allowing them to model nonlinear decision boundaries or functions.

C: Emergent Garden
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