Create a computer vision system using decision tree algorithms to solve a real-world problem : Activation Functions

The video tutorial covers different activation functions used in neural networks, including sigmoid, ReLU, and hyperbolic tangent functions. It explains the characteristics, applications, and advantages of each function. The sigmoid function is used for binary classification, converting inputs to a range between 0 and 1. ReLU is preferred in hidden layers to avoid the vanishing gradient problem, while the hyperbolic tangent function ranges from -1 to 1, offering an alternative to sigmoid. The tutorial concludes with a brief overview of the functions and prepares students for building a perceptron model in Python.

What is the primary range of values for the sigmoid activation function?

• 0 to 1

• -1 to 1

• 0 to 2

• -1 to 0

In which layer is the sigmoid activation function typically used?

• Output layer

• All layers

• Hidden layer

• Input layer

What is a key advantage of the ReLU activation function?

• It is computationally expensive

• It avoids the vanishing gradient problem

• It saturates quickly

• It is used only in output layers

What is the range of the hyperbolic tangent activation function?

• 0 to 1

• -1 to 1

• -2 to 2

• 0 to 2

Why might the hyperbolic tangent function be preferred over the sigmoid function?

• It is used in input layers

• It is more prone to the vanishing gradient problem

• It is less computationally efficient

• It has a wider range