What is the role of activation functions in deep learning?

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Krishnapriya Rajeev · Answer

Activation functions are functions that are applied to the output of each neuron in a neural network, transforming the input signal into an output signal that is passed on to the next layer of the network.

The primary purpose of activation functions is to introduce nonlinearity into the model, which is necessary for the neural network to learn complex patterns and relationships in the data. Without activation functions, a neural network would be a linear model, which would limit its ability to represent complex patterns in the data.

Some of the most commonly used activation functions in deep learning are:

ReLU (Rectified Linear Unit): ReLU is a simple and efficient activation function that sets any negative input values to zero and leaves positive input values unchanged. It is commonly used in deep learning due to its simplicity and computational efficiency.
Sigmoid: The sigmoid function maps any input value to a value between 0 and 1, which is useful for binary classification problems. However, it suffers from the problem of vanishing gradients and is less commonly used in modern deep-learning architectures.
Tanh (Hyperbolic tangent): Tanh is similar to the sigmoid function, but maps the input to a value between -1 and 1. It is useful for classification problems with multiple classes.
Softmax: Softmax is a specialized activation function used for multi-class classification problems. It maps the input to a probability distribution over the output classes.

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What is the role of activation functions in deep learning?

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