Activation functions play a crucial role in the design of neural networks. They determine the output of a neuron based on its input, essentially deciding whether or not the information received is relevant for making predictions. The activation function introduces non-linear properties to the network, which allows it to learn from more complex data.
There are several types of activation functions used in neural networks, each with their own unique characteristics and applications. The most commonly used activation functions include sigmoid, tanh, ReLU (Rectified Linear Unit), Leaky ReLU, and softmax.
The sigmoid function outputs values between 0 and 1. This makes it particularly useful neural network for texts models where we have to predict probabilities as outcomes since probability lies in this range. However, sigmoid has two main drawbacks: vanishing gradients and output not zero-centered.
Tanh or hyperbolic tangent function is similar to the sigmoid but outputs values between -1 and 1. This means that the output is zero-centered which helps with some optimization algorithms during training but still suffers from vanishing gradients problem.
ReLU stands out due to its computational efficiency by simply thresholding a matrix of activations at zero which means all negative inputs will be mapped to zero while positive inputs remain unchanged. It solves the problem of vanishing gradients allowing models to learn faster and perform better but has a drawback known as dying ReLU where neurons can sometimes produce only zero outputs causing them to die for all subsequent layers during training process.
Leaky ReLU addresses this issue by having a small slope instead of an absolute zero when x < 0 thus keeping neurons alive even for negative input values by allowing small negative responses when the input is less than zero.
Softmax function comes handy when dealing with multiclass classification problems as it converts scores into probabilities that sum up to one across all classes providing an interpretability advantage over other activation functions.
Choosing an appropriate activation function depends on several factors such as type of problem you’re trying to solve, the computational efficiency you’re aiming for, and the behavior of the function itself. Some functions work better with certain types of data or in specific layers within the network.
In conclusion, activation functions are a critical part of neural networks. They introduce non-linearity into the model allowing it to learn complex patterns from data. With advancements in deep learning research and application, new activation functions continue to emerge offering solutions that improve upon existing ones making it an exciting area of study for anyone interested in machine learning and artificial intelligence.