loss functions

loss-functions

Loss functions are essential components in machine learning and deep learning, guiding the training process of models by quantifying the difference between predicted and actual outcomes. They serve as objective functions that algorithms aim to minimize or maximize, ultimately improving model performance. Various types of loss functions exist, each suited for different tasks, such as regression or classification. Common examples include Mean Squared Error (MSE) for regression tasks and Cross-Entropy Loss for classification tasks. Understanding and selecting the appropriate loss function is crucial for developing effective machine learning models.

What are Loss Functions?

Towards Data Science

An article explaining different most used loss function in deep learning

Machine Learning Glossary

Loss Functions Cross-Entropy Hinge Huber Kullback-Leibler RMSE MAE (L1) MSE (L2) Cross-Entropy Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a pro...

Loss Functions in Machine Learning

Towards Data Science

Loss functions have an important role in machine learning as they guide the learning process of the model and define its objective. There is a large number of loss functions available and choosing…

Loss Functions: An Algorithm-wise Comprehensive Summary

Daily Dose of Data Science

Loss functions are a key component of ML algorithms. They specify the objective an algorithm should aim to optimize during its training. In other words, loss functions tell the algorithm what it shoul...

A Single Frame Summary of 10 Most Common Regression and Classification Loss Functions

Daily Dose of Data Science

Loss Functions in Neural Networks

Becoming Human: Artificial Intelligence Magazine

Loss functions show how deviated the prediction is with actual prediction. Machines learn to change/decrease loss function by moving close to the ground truth. There are many functions out there to…

Most Common Loss Functions in Machine Learning

Towards Data Science

As a core element, Loss function is a method of evaluating your Machine Learning algorithm that how well it models your featured dataset. It is defined as a measurement of how good your model is in…

Built-in loss functions

Codecademy

In PyTorch, loss functions are critical in the training process of deep learning models. They measure how well the model’s predictions match the ground truth. PyTorch provides several built-in loss fu...

Loss Functions and Their Use In Neural Networks

Towards Data Science

Overview of loss functions and their implementations Photo by Chris Ried on Unsplash Loss functions are one of the most important aspects of neural networks, as they (along with the optimization func...

The Mathematics of Loss Functions in Machine Learning

The Pythoneers

Introduction to Loss Functions In machine learning, how well predictive models work depends largely on their ability to reduce errors in their predictions. At the heart of this process are loss funct...

JAX Loss Functions

Towards AI

Loss functions are at the core of training machine learning. They can be used to identify how well the model is performing on a dataset. Poor performance leads to a very high loss, while a well-perfor...

Loss functions based on feature activation and style loss.

Towards Data Science

Loss functions using these techniques can be used during the training of U-Net based model architectures and could be applied to the training of other Convolutional Neural Networks that are…