The video explains the concept of activation functions in deep neural networks, emphasizing their role in preventing the collapse of the network into a single neuron. It highlights the importance of nonlinear functions in maintaining the representation power of multilayered networks. The video concludes with a brief mention of upcoming content on different types of activation functions.

Why is an activation function necessary in a neural network?

• To increase the speed of computation

• To prevent the network from collapsing into a single neuron

• To reduce the number of layers in the network

• To ensure the network uses only linear functions

What happens if a neural network does not use an activation function?

• The network becomes more complex

• The network uses more memory

• The network collapses into a single neuron

• The network becomes faster

What property of matrix multiplication is discussed in relation to activation functions?

• Identity property

• Associative property

• Commutative property

• Distributive property

Why are non-linear functions used as activation functions?

• They are easier to compute

• They prevent the network from collapsing

• They reduce the number of neurons needed

• They increase the speed of training

What is the role of an activation function in a neural network?

• To act as a barrier preventing matrix multiplication collapse

• To increase the number of layers

• To ensure the network is linear

• To reduce the computational cost