Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Properties of Activat

The video explains the importance of activation functions in neural networks, emphasizing their role in introducing nonlinearity, which enhances the network's representational power. It discusses practical choices for activation functions, highlighting the common use of a single function across a network, except for the output layer. The video details the sigmoid and ReLU functions, noting their properties and computational aspects. It also outlines the essential properties of activation functions, such as nonlinearity, ease of computation, and differentiability, and demonstrates their implementation in Torch.

Why is nonlinearity crucial in activation functions for neural networks?

• To ensure neurons can learn different features

• To make the network faster

• To simplify the network architecture

• To reduce the number of neurons

What is a common practice regarding activation functions in neural networks?

• Changing activation functions dynamically during training

• Using no activation functions at all

• Applying the same activation function throughout the network

• Using a different activation function for each neuron

Which activation function is known for its simplicity and efficiency?

• Tanh

• Sigmoid

• ReLU

• Softmax

What is a key property of the Sigmoid activation function?

• It outputs values between -1 and 1

• It is non-differentiable

• It is linear

• It outputs values between 0 and 1

What is a necessary property of activation functions for training neural networks?

• They must be non-differentiable

• They should be complex to compute

• They must be differentiable

• They should be linear

Why is differentiability important for activation functions?

• To ensure the network is fast

• To compute gradients for learning

• To simplify the network

• To reduce the number of layers

Which property is NOT essential for an activation function?

• Differentiability

• Ease of computation

• Being non-differentiable

• Nonlinearity