loss functions

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 Part-2

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As we all know that for regression problems we use Least square error as the loss function. Through this, we get a convex loss function and we can optimize by finding its global minimal. But when it…

Loss Functions in Machine Learning

Towards Data Science

Understand the most common loss functions and when to use each one Continue reading on Towards Data Science

What are Loss Functions?

Towards Data Science

An article explaining different most used loss function in deep learning

A Quick Guide to Loss Functions

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Types of Loss functions used in Classification and Regression problems

Machine Learning 103: Loss Functions

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In two previous articles I covered two of the most basic models used in machine learning — linear regression and logistic regression. In both cases, we were interested in searching for the set of…

A Comprehensive Guide To Loss Functions — Part 1 : Regression

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Loss functions are used to calculate the difference between the predicted output and the actual output. To know how they fit into neural networks, read :

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...

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...

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…

Classification Loss Functions: Intuition and Applications

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A simpler way to understand derivations of loss functions for classification and when/how to apply them in PyTorch Source: GPT4o Generated Whether you are new to exploring neural networks or a season...

An Algorithm-wise Summary of Loss Functions in Machine Learning

Daily Dose of Data Science

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

Optimization: Loss Function Under the Hood (Part III)

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Continuing this journey, I have discussed the loss function and optimization process of linear regression at Part I, logistic regression at part II, and this time, we are heading to Support Vector…

Understanding loss functions : Hinge loss

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Often in Machine Learning we come across loss functions. For someone like me coming from a non CS background, it was difficult for me to explore the mathematical concepts behind the loss functions…

Understanding Entropy, Loss Functions and the Mathematical intuition behind them

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A Loss Function is an essential step in any Deep Learning Problem. First of all, what is a loss function? A loss function gives an idea about how good our classifier is and it does quantify how happy…...

Common Loss functions in machine learning

Towards Data Science

Machines learn by means of a loss function. It’s a method of evaluating how well specific algorithm models the given data. If predictions deviates too much from actual results, loss function would…

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

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...

Overview of loss functions for Machine Learning

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Machine learning is, at its core, an optimization problem. With any optimization problem, we need ways to calculate how far our predictions are from the truth to determine in what direction we need…

Keras Losses Functions

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Hi! Let’s dig a little deeper today into those neural networks, what do you think? Let’s first find out why loss functions are used and then what they mean. And I can’t help but say this. With the…

Introduction of Different types of Loss Functions in Machine learning and Deep learning

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In the field of deep learning and Machine learning, the “Loss” is the forfeit of poor prediction. That means the Loss suggested how much satisfactory or awful prediction of the model is. If the Loss…

Loss Functions in TensorFlow

Machine Learning Mastery

Last Updated on July 15, 2022 Loss metric is very important for neural networks. As all machine learning model is a optimization problem or another, the loss is the objective function to minimize. In ...

Loss Functions -when to use which one

Towards Data Science

The ultimate goal of all algorithms of machine learning is to decrease loss. Loss has to be calculated before we try strategy to decrease it using different optimizers. Loss Function is an error in 1…...

Creating Custom Loss Functions in TensorFlow: Understanding the Theory and Practicalities

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Maximizing Model Performance with Custom Loss Functions in TensorFlow Continue reading on Towards Data Science

MSE or MAE? Which and Why? Loss Functions used in Regression and Classification.

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In Neural Networks (NNs), the loss function is critical to understanding the model’s performance. The loss function must be chosen carefully while constructing and configuring NN models. And the…