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Addendum

Forward-Forward Algorithm
https://medium.com/@Mosbeh_Barhoumi/forward-forward-algorithm-ac24d0d9ffd

The forward-forward algorithm uses a custom loss function that compares the mean square value of the activations for positive and negative samples.
The network optimizes this loss function by performing gradient calculations and optimization steps on the trainable weights of the dense layer.
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#ActivationFunction #ForwardPropagation #NeuralNetworks

5. "Why would one avoid using a linear #ActivationFunction in a #NeuralNetwork?" #AI

No, #ChatGPT3 #GPT3. The derivative of a linear activation function is *always* positive; it has no vanishing gradient. The problem it has is that you can't backpropagate (constant derivative) and can mathematically reduce a network with linear functions down to a single layer.