simple_neural_network | A very simple neural network in python
kandi X-RAY | simple_neural_network Summary
kandi X-RAY | simple_neural_network Summary
simple_neural_network is a Python library. simple_neural_network has no bugs, it has no vulnerabilities and it has low support. However simple_neural_network build file is not available. You can download it from GitHub.
A very simple neural network in python. This program takes as input different combinations of 0 and 1 and tries to predict output a value close to 0 or 1 after seeing the actual output of the combinations. Basically, we first let the network "try" to predict the output given the input. We then see how it performs so that we can adjust it to do a bit better for each iteration. Input: 0 0 1 output: 0. Input: 0 1 1 output: 0. Input: 1 0 1 output: 1. Input: 1 1 1 output: 1. Here the input dataset has two parts: One is the combinations vector (X) and the other is the actual output vector (y). The aim is to train a simple 2 layer Neural Network to predict the correct output (a value close to the true output). The training helps to adjust weights at each iteration thus getting closer to true value each time by delta change in weight.
A very simple neural network in python. This program takes as input different combinations of 0 and 1 and tries to predict output a value close to 0 or 1 after seeing the actual output of the combinations. Basically, we first let the network "try" to predict the output given the input. We then see how it performs so that we can adjust it to do a bit better for each iteration. Input: 0 0 1 output: 0. Input: 0 1 1 output: 0. Input: 1 0 1 output: 1. Input: 1 1 1 output: 1. Here the input dataset has two parts: One is the combinations vector (X) and the other is the actual output vector (y). The aim is to train a simple 2 layer Neural Network to predict the correct output (a value close to the true output). The training helps to adjust weights at each iteration thus getting closer to true value each time by delta change in weight.
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simple_neural_network 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.
simple_neural_network has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of simple_neural_network is current.
Quality
simple_neural_network has no bugs reported.
Security
simple_neural_network has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
simple_neural_network 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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simple_neural_network releases are not available. You will need to build from source code and install.
simple_neural_network has no build file. You will be need to create the build yourself to build the component from source.
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simple_neural_network Key Features
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simple_neural_network Examples and Code Snippets
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Community Discussions
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Vulnerabilities
No vulnerabilities reported
Install simple_neural_network
You can download it from GitHub.
You can use simple_neural_network 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 simple_neural_network 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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