Deep Learning - Crash Course 2023 - Summary on Activation Functions

The video tutorial provides an overview of activation functions used in neural networks, including sigmoid, Relu, Selu, swish, and softmax. It explains the role of these functions in scaling data and generating outputs in the required range. The softmax function is highlighted for its use in multiclass classification tasks. The tutorial concludes with advice on selecting the right activation function for different tasks.

Which of the following activation functions is known for its ability to handle vanishing gradient problems?

• Tanh

• Softmax

• ReLU

• Sigmoid

What is the primary use of the softmax activation function?

• To handle vanishing gradient problems

• To generate probabilities for multiclass classification

• To perform binary classification

• To scale data to a range between 0 and 1

In which layer is the softmax activation function typically used?

• Input layer

• All layers

• Output layer

• Hidden layer

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

• To initialize weights

• To reduce the number of neurons

• To modify the weighted sum and generate output in a required range

• To increase the number of layers

What should you consider when choosing an activation function for a specific task?

• The specific requirements of the task

• The number of neurons in the network

• The mathematical complexity of the function

• The color of the neural network