Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Why Activation Functi

The video tutorial discusses the structure of a neural network, focusing on the use of linear activations in all layers except the output layer, which uses a sigmoid activation. It explores the variability in the number of neurons in hidden layers and poses the question of whether such a model can still be considered a neural network. The exercise encourages viewers to think critically about the definition and characteristics of neural networks.

What happens to the input features as they pass through the layers of a neural network?

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How do the number of neurons in hidden layers compare to the output layer in a neural network?

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Explain the significance of having linear activations in all hidden layers except the output layer.

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Discuss whether a model with linear activations everywhere except the last layer can still be classified as a neural network.

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What is the role of the output layer in a neural network with linear activations in the hidden layers?

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