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 is unique about the output layer in the discussed neural network model?

• It has multiple neurons.

• It uses a linear activation function.

• It has a sigmoid activation function.

• It is the first layer in the network.

In the described neural network, what is the role of the hidden layers?

• They are not part of the network.

• They contain the output neuron.

• They have linear activations.

• They perform non-linear transformations.

What is the activation function used in the output layer of the model?

• ReLU

• Sigmoid

• Tanh

• Linear

How are the input features processed in the hidden layers of the network?

• Through a non-linear transformation.

• By applying a sigmoid function.

• Using a weighted sum or dot product.

• By ignoring them completely.

What is the main question posed about the neural network model?

• Whether it can have multiple output neurons.

• If it can be considered a neural network with linear activations except the last layer.

• Whether it should have non-linear activations in all layers.

• If it can function without an input layer.