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Loss Functions Part-2
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…
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Loss Functions
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...
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What are Loss Functions?
An article explaining different most used loss function in deep learning
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Loss Functions in Machine Learning
Understand the most common loss functions and when to use each one Continue reading on Towards Data Science
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A Comprehensive Guide To Loss Functions — Part 1 : Regression
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 :
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Loss Functions and Their Use In Neural Networks
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...
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Machine Learning 103: Loss Functions
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…
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A Quick Guide to Loss Functions
Types of Loss functions used in Classification and Regression problems
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Optimization: Loss Function Under the Hood (Part II)
This series aims to explain loss functions of a few widely-used supervised learning models, and some options of optimization algorithms. In part I, I walked through the optimization process of Linear…...
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Loss Functions in Neural Networks
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…
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Loss Functions: An Algorithm-wise Comprehensive Summary
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...
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Loss Functions in TensorFlow
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 ...
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An Algorithm-wise Summary of Loss Functions in Machine Learning
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 ...
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Loss Function Analysis with R
Manufactured products are defined by the quality of their features. However, only some of them are relevant for customers; these are called CTQ (critical to quality) characteristics. Every process…
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Understanding Entropy, Loss Functions and the Mathematical intuition behind them
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…...
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Understanding loss functions : Hinge loss
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…
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Implementing Custom Loss Functions in PyTorch
Understanding the theory and implementation of custom loss functions in PyTorch using the MNIST dataset Continue reading on Towards Data Science
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Creating Custom Loss Functions in TensorFlow: Understanding the Theory and Practicalities
Maximizing Model Performance with Custom Loss Functions in TensorFlow Continue reading on Towards Data Science
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A Single Frame Summary of 10 Most Common Regression and Classification Loss Functions
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...
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Cross-Entropy Loss Function
When working on a Machine Learning or a Deep Learning Problem, loss/cost functions are used to optimize the model during training. The objective is almost always to minimize the loss function. The…
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Loss and Loss Functions for Training Deep Learning Neural Networks
Last Updated on October 23, 2019 Neural networks are trained using stochastic gradient descent and require that you choose a loss function when designing and configuring your model. There are many los...
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What’s the Difference Between a Metric and a Loss Function?
…and why does your machine learning project need both? Continue reading on Towards Data Science
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MSE or MAE? Which and Why? Loss Functions used in Regression and Classification.
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…
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Keras Losses Functions
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…
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