How ReLU Enables Neural Networks to Approximate Continuous Nonlinear Functions?

Learn how a neural network with one hidden layer using ReLU activation can represent any continuous nonlinear functions.

Activation functions play an integral role in Neural Networks (NNs) since they introduce non-linearity and allow the network to learn more complex features and functions than just a linear regression. One of the most commonly used activation functions is Rectified Linear Unit (ReLU), which has been theoretically shown to enable NNs to approximate a wide range of continuous functions, making them powerful …

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