understanding_loss_functions | Role of loss functions in Parameterized Learning

by shilparai Python Version: Current License: No License

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kandi X-RAY | understanding_loss_functions Summary

understanding_loss_functions is a Python library. understanding_loss_functions has no bugs, it has no vulnerabilities and it has low support. However understanding_loss_functions build file is not available. You can download it from GitHub.

Role of loss functions in Parameterized Learning

Support

Quality

Security

License

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Support

understanding_loss_functions has a low active ecosystem.

It has 0 star(s) with 0 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

understanding_loss_functions has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of understanding_loss_functions is current.

Quality

understanding_loss_functions has no bugs reported.

Security

understanding_loss_functions has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

understanding_loss_functions does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

understanding_loss_functions releases are not available. You will need to build from source code and install.

understanding_loss_functions has no build file. You will be need to create the build yourself to build the component from source.

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understanding_loss_functions Key Features

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understanding_loss_functions Examples and Code Snippets

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Vulnerabilities

No vulnerabilities reported

Install understanding_loss_functions

You can download it from GitHub.
You can use understanding_loss_functions like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

SVM loss is the commonly used loss function. Simple idea behind SVM loss is that, it tends to have higher scores for the correct class of ith image and lower scores to the incorrect classes. This loss function is also called Hinge Loss Function. multiclass_support_vector.py shows the implementation of SVM loss function for cat-dog datasets.

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