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.

Describe the difference between the sigmoid and hyperbolic tangent activation functions.

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In what scenarios would you use the hyperbolic tangent activation function over the sigmoid function?

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