CIFAR-10-binary-classification | 2-class dataset extracted from CIFAR-10
kandi X-RAY | CIFAR-10-binary-classification Summary
kandi X-RAY | CIFAR-10-binary-classification Summary
CIFAR-10-binary-classification is a Python library. CIFAR-10-binary-classification has no bugs, it has no vulnerabilities and it has low support. However CIFAR-10-binary-classification build file is not available. You can download it from GitHub.
2-class dataset extracted from CIFAR-10. The data has 10,000 training examples in 3072 dimensions and 2,000 testing examples. This code describes the implementation details and results of a neural network consisting of one hidden layer fully connected neural network. The code has been written from scratch, includes the functions for calculating gardients, weights updates, forward pass and backpropogation.
2-class dataset extracted from CIFAR-10. The data has 10,000 training examples in 3072 dimensions and 2,000 testing examples. This code describes the implementation details and results of a neural network consisting of one hidden layer fully connected neural network. The code has been written from scratch, includes the functions for calculating gardients, weights updates, forward pass and backpropogation.
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Security
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Support
CIFAR-10-binary-classification has a low active ecosystem.
It has 0 star(s) with 0 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
CIFAR-10-binary-classification has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of CIFAR-10-binary-classification is current.
Quality
CIFAR-10-binary-classification has 0 bugs and 0 code smells.
Security
CIFAR-10-binary-classification has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
CIFAR-10-binary-classification code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
CIFAR-10-binary-classification 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.
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CIFAR-10-binary-classification releases are not available. You will need to build from source code and install.
CIFAR-10-binary-classification has no build file. You will be need to create the build yourself to build the component from source.
It has 191 lines of code, 13 functions and 1 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed CIFAR-10-binary-classification and discovered the below as its top functions. This is intended to give you an instant insight into CIFAR-10-binary-classification implemented functionality, and help decide if they suit your requirements.
- Backward propagation .
- Train the network .
- Calculate the gradient of the derivative at the given position .
- Evaluate the model .
- Initialize the model .
- Sigmoid function .
- Computes the cross entropy of the log - likelihood .
Get all kandi verified functions for this library.
CIFAR-10-binary-classification Key Features
No Key Features are available at this moment for CIFAR-10-binary-classification.
CIFAR-10-binary-classification Examples and Code Snippets
No Code Snippets are available at this moment for CIFAR-10-binary-classification.
Community Discussions
No Community Discussions are available at this moment for CIFAR-10-binary-classification.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install CIFAR-10-binary-classification
You can download it from GitHub.
You can use CIFAR-10-binary-classification 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.
You can use CIFAR-10-binary-classification 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
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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