Reinforcement Learning and Deep RL Python Theory and Projects - DNN Why Activation Function Is Required Exercise

The video tutorial discusses the structure of a neural network where all layers except the output layer have linear activations. The output layer contains a single neuron with a sigmoid activation function. The tutorial explores whether such a model can still be considered a neural network, given the linear activations in the hidden layers. It also covers the variability in the number of neurons across different hidden layers.

What happens to the input features in the input layer of a neural network?

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Is a model with linear activations everywhere except the last layer still considered a neural network? Explain your reasoning.

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