Neural-Network-with-Python | neural network with 3 layers | Machine Learning library

 by   jiexunsee Python Version: Current License: No License

kandi X-RAY | Neural-Network-with-Python Summary

kandi X-RAY | Neural-Network-with-Python Summary

Neural-Network-with-Python is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Numpy, Neural Network applications. Neural-Network-with-Python has no bugs, it has no vulnerabilities and it has low support. However Neural-Network-with-Python build file is not available. You can download it from GitHub.

A neural network with 3 layers made with just numpy as dependency
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              Neural-Network-with-Python has a low active ecosystem.
              It has 24 star(s) with 27 fork(s). There are 3 watchers for this library.
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              It had no major release in the last 6 months.
              There are 1 open issues and 1 have been closed. On average issues are closed in 20 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of Neural-Network-with-Python is current.

            kandi-Quality Quality

              Neural-Network-with-Python has 0 bugs and 0 code smells.

            kandi-Security Security

              Neural-Network-with-Python has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              Neural-Network-with-Python code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              Neural-Network-with-Python does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              Neural-Network-with-Python releases are not available. You will need to build from source code and install.
              Neural-Network-with-Python has no build file. You will be need to create the build yourself to build the component from source.
              Neural-Network-with-Python saves you 18 person hours of effort in developing the same functionality from scratch.
              It has 51 lines of code, 5 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 Neural-Network-with-Python and discovered the below as its top functions. This is intended to give you an instant insight into Neural-Network-with-Python implemented functionality, and help decide if they suit your requirements.
            • Train the neural network .
            • Initializes the initial weights .
            • Forward the forward pass through the network .
            • Sigmoid function .
            • Derivative of the sigmoid derivative .
            Get all kandi verified functions for this library.

            Neural-Network-with-Python Key Features

            No Key Features are available at this moment for Neural-Network-with-Python.

            Neural-Network-with-Python Examples and Code Snippets

            No Code Snippets are available at this moment for Neural-Network-with-Python.

            Community Discussions

            Trending Discussions on Neural-Network-with-Python

            QUESTION

            How is this simple Keras neural-network calculating result?
            Asked 2018-Jul-26 at 05:02

            I'm trying to understand how a simple forward neural network works... starting off with the example here I have simplified it to make a trainer that generally gives a 100% accurate "AND" neuron:

            ...

            ANSWER

            Answered 2018-Jul-26 at 05:02

            The result you see in your print statements are after the softmax operation.

            [1 1] . [-.70 .77; -.64 .81] + [1.9 -0.62] = [.53 .96]

            Then

            exp(.53) = 1.69, exp(.96) = 2.62

            so result is

            [1.69/(1.69+2.62) 2.62/(1.69+2.62)] = [.39 .61]

            (obviously with rounding errors)

            Also note that before taking the softmax technically you have a relu activation, but since that is the identity for positive numbers, it doesn't have an effect for the [1 1] example. It would make a difference for the [1 0] example, as the second component of your Wx + b is negative, which would then be zeroed out.

            Source https://stackoverflow.com/questions/51530846

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install Neural-Network-with-Python

            You can download it from GitHub.
            You can use Neural-Network-with-Python 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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