A Comprehensive Guide to Loss Functions🔥: The Backbone of Machine Learning

Author(s): Asad iqbal

Originally published on Towards AI.

Our detailed guide will help you understand the importance of loss functions in machine learning. It will help you distinguish between loss and cost functions, the different kinds, such as MSE and MAE, and how they are used in machine learning tasks.

This member-only story is on us. Upgrade to access all of Medium.

Imagine you’re a data scientist training a machine learning model to predict house prices. You’ve collected data, chosen a model, and started training. But how do you measure its success? Are its predictions accurate or not? The solution is in loss functions, an important component of machine learning that helps models learn from their mistakes.

This article will explain what they are, how they work, and their benefits and challenges.

Not a member? Read for free!

1. Softmax activation function

2. Sigmoid / Logistic activation function

3. Hyperbolic Tangent (Tanh) Activation Function

4. Rectified Linear Unit (ReLU)Activation Function

5. Leaky ReLU Activation Function

A loss function, also known as a cost function or error function, measures how well a machine learning model predicts the expected outcome. It quantifies the difference between the predicted and actual values in a dataset. Essentially, a loss function assigns an actual number to summarize a prediction’s good and bad elements.

The primary goal of a loss function is to minimize the error between predictions and actuals, allowing the model to improve its performance over time.

Loss functions operate by comparing the predicted value (output of the model) with the actual value (ground… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI