Exploring Activation and Loss Functions in Machine Learning

Exploring Activation and Loss Functions in Machine Learning

A guide to the most frequently used activation and loss functions, and a breakdown of their benefits and limitations. In this post, we’re going to discuss the most widely-used activation and loss functions for machine learning models.

In this post, we’re going to discuss the most widely-used activation and loss functions for machine learning models. We’ll take a brief look at the foundational mathematics of these functions and discuss their use cases, benefits, and limitations.

Without further ado, let’s get started!

What is an Activation Function?

To learn complex data patterns, the input data of each node in a neural network passes through a function the limits and defines that same node’s output value. In other words, it takes in the output signal from the previous node and converts it into a form interpretable by the next node. This is what an activation function allows us to do.

Need for an Activation function

  1. Restricting value: The activation function keeps the values from the node restricted within a certain range, because they can become infinitesimally small or huge depending on the multiplication or other operations they go through in various layers (i.e. the vanishing and exploding gradient problem).
  2. Add non-linearity: *In the absence of an activation function, the operations done by various functions can be considered as stacked over one another, which ultimately means a linear combination of operations performed on the input.Thus, a *neural network without an activation function is essentially a linear regression model.

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