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

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 necessity of nonlinear functions to maintain the representation power of multi-layered networks. The video also touches on the associative property of matrix multiplication and how it affects network layers. Finally, it sets the stage for further exploration of different types of activation functions and their implementation in Python.

Why is an activation function necessary in a neural network?

• To prevent the network from collapsing into a single neuron

• To ensure the network uses only linear functions

• To increase the speed of computation

• To reduce the number of layers in the network

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

• The network uses more memory

• The network becomes faster

• The network collapses into a single neuron

• The network becomes more complex

What property of matrix multiplication is discussed in relation to network collapse?

• Associative property

• Identity property

• Distributive property

• Commutative property

What type of function is required to prevent a neural network from collapsing into a single neuron?

• Quadratic function

• Exponential function

• Linear function

• Nonlinear function

What is the main benefit of using nonlinear activation functions in neural networks?

• They simplify the network architecture

• They increase the number of neurons required

• They enhance the network's representation power

• They reduce the training time