Deep Learning - Deep Neural Network for Beginners Using Python - Other Activation Functions

The video tutorial discusses various activation functions used in neural networks, including sigmoid, tanh, and ReLU. It explains the properties and formulas of these functions, highlighting their differences and applications. Sigmoid and tanh are compared, noting their output ranges and impact on the vanishing gradient problem. ReLU is introduced as a simple yet widely used function, with examples illustrating its behavior. The tutorial concludes with a summary of these activation functions and mentions other available options in the data science community.

What are the extreme points of the sigmoid activation function?

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Explain the formula for the tanh activation function.

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What does the ReLU activation function do to negative values?

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How does the output of the ReLU function change for positive values?

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List some other activation functions that are available in the data science community.

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