https://github.com/toelt-llc/paper-nn-performance-bootstrap-tutorial

This repository contains material that has been linked in the MAKE paper on bootstrapping. No paper files are contained here. Only code.
https://github.com/toelt-llc/paper-nn-performance-bootstrap-tutorial

paper python tutorial

Last synced: 1 day ago
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This repository contains material that has been linked in the MAKE paper on bootstrapping. No paper files are contained here. Only code.

• Host: GitHub
• URL: https://github.com/toelt-llc/paper-nn-performance-bootstrap-tutorial
• Owner: toelt-llc
• License: gpl-3.0
• Created: 2021-01-08T13:58:04.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2021-01-22T08:58:06.000Z (about 4 years ago)
• Last Synced: 2024-12-29T23:55:15.042Z (about 2 months ago)
• Topics: paper, python, tutorial
• Language: Jupyter Notebook
• Homepage:
• Size: 76.2 KB
• Stars: 0
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Code for _Estimating Neural Network's Performance with Bootstrap: a Tutorial_

This repository contains the code that have been used for the paper 

Michelucci, U. and Venturini, F., **Estimating Neural Network's Performance with Bootstrap: a Tutorial**, to be published on the journal [MAKE](https://www.mdpi.com/journal/make) (MDPI).

## How to use this repository

The repository contains a folder, ```code``` that contains one Jupyter Notebook where you will find the code with some examples of the simulations as they are described in the paper. Clicking on the link below

[Simulation Python Code](http://colab.research.google.com/github/toelt-llc/NN-Performance-Bootstrap-Tutorial/blob/main/code/Tutorial%20Resampling%20with%20NN.ipynb)

you can open the notebook in [Google Colab](https://colab.research.google.com) where you can try the simulations. Note that if you decide to use Google Colab, you don't need to install anything on your laptop, except [Google Chrome](https://www.google.com/chrome/?brand=FHFK&gclid=CjwKCAiAouD_BRBIEiwALhJH6JKt7h3q1IjeBhWPLfJXed4YSZ9qqVwIo8DfPTp5j4PNM1hRPUeaPhoCnXoQAvD_BwE&gclsrc=aw.ds). If you have a python installation on your machine (for example Anaconda for MS Windows), you can clone this repository locally and use it on your machine.

## References

### Paper

If you want to reference the paper you can use the following formats

**Plain Text**: 

    Michelucci, U.; Venturini, F. Estimating Neural Network’s Performance with Bootstrap: a Tutorial. 

    Preprints 2021, 2021010431 

    

(note that as soon as the paper will be published the reference will be changed)

### This repository

If you use any of this code, a reference would be nice. You can reference it using the following texts

**Plain Text**: 

    

    Michelucci, U. Code for Estimating Neural Network’s Performance with Bootstrap: 

    a Tutorial, https://github.com/toelt-llc/NN-Performance-Bootstrap-Tutorial, 2021

**bibtex**:

    @misc{code,

      author = {Michelucci, U.},

      title = {Code for {\sl Estimating Neural Network's Performance with Bootstrap: a Tutorial}},

      year = {2021},

      publisher = {GitHub},

      journal = {GitHub repository},

      howpublished = {\url{https://github.com/toelt-llc/NN-Performance-Bootstrap-Tutorial}

    }}