Deep Learning Activation Function

Deep Learning Activation Function

Deep Learning Activation Function

The activation function is an essential component of deep learning algorithms. It introduces non-linearity into the model, which is required for the model to learn complex and non-linear relationships between inputs and outputs.

An activation function Is a mathematical equation that determines the output of a neuron based on the sum of its inputs. The output of an activation function is usually a non-linear transformation of its input. Further, the activation function compares the input value to a threshold value. If the input value is greater than the threshold value, the neuron is activated. It's disabled if the input value is less than the threshold value, which means its output isn't sent on to the next or hidden layer.

The most commonly used activation functions are Sigmoid, ReLU, and Tanh.

1. Sigmoid is a smooth function that maps any input to a value between 0 and 1. It is commonly used in the output layer of binary classification problems where the model output needs to be interpreted as a probability.
2. ReLU (Rectified Linear Unit) is the most widely used activation function. It is a piecewise linear function that returns the input if it is positive, and 0 if it is negative. It is computationally efficient and has been found to work well in practice.
3. Tanh is similar to sigmoid but maps the input to a value between -1 and 1. It is commonly used in the output layer of regression problems.

Choosing the right activation function can significantly impact the performance of a deep learning model. It is important to experiment with different activation functions to see which one works best for the given problem.