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Defining and Understanding Loss Functions for Machine Learning | HugeCount

The way we approach difficult problems is also evolving as a result of machine learning’s widespread adoption. The idea of a loss function in machine learning model. In this guest post, we’ll dive into the fascinating topic of loss functions, discussing their role in machine learning, the many kinds of loss functions, and how they affect the training process. The goal of a loss function is to quantify how well or poorly the model is performing. The goal during training is to minimize this loss function, which is achieved by adjusting the model’s internal parameters, loss function in machine learning, […]

Source: https://hugecount.com/education/defining-and-understanding-loss-functions-for-machine-learning/

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Loss function is a method of evaluating how well your algorithm models your dataset. Learn more

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Loss Functions or Cost Function - EXPLAINED under 14 minute | Cross Entr...

#data science #machine learning #loss function #cost function #artificial intelligence #neural network loss function #machine learning loss function #cross entropy #cross entropy loss function

Common Loss functions and their use - quick note

Common Loss functions and their use – quick note

Machines learn by means of a loss function which reflects how well a specific model performs with the given data. If predictions deviate too much from actual results, loss function would yield a very large value. Gradually, with function, parameters are modified accordingly to reduce the error in prediction. In this article, we will quickly review some common loss functions and their usage in the…

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If an economist is making, say, a calculation of purchasing power parity between South Africa and the United States over the past century, she would not be much troubled by a failure of fit of, say, plus or minus 8 percent. If her purpose were merely to show that prices corrected for exchange rates do move roughly together and that therefore a country-by-country macroeconomics of inflation would be misleading for many purposes, such a crude level of accuracy does the job. Maybe plus or minus 20 percent would do it. But someone arbitraging between the dollar and the rand over the next month would not be so tranquil if his prediction were off by as little as 1 percent, maybe by as little as 1/10 of 1 percent, especially if he were leveraged and unhedged and had staked his entire net wealth on the matter.

DNM

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Loss functions

Hinge loss $$\max \{0,1-y_n w^T x_n \}$$

Log loss $$\log [1+ \exp{(-y_n w^T x_n) }]$$

Exponential loss $$\exp{(-y_n w^T x_n) }$$

Object localization $$ \Delta(y,y')=1- \frac{|y \cap y'|}{|y \cup y'|}$$

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