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Cited by 2Undergraduate Neuroscience Education and ResearchComputational Physics and Python ApplicationsNeural Networks and Applications

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EpyNN: Educational python for Neural Networks

Open access
Malard, Florian, Danner, Laura, Rouzies, Emilie, Meyer, Jesse G., Lescop, Ewen, Olivier-Van Stichelen, Stéphanie·2022·SoftwareX
DOI 10.1016/j.softx.2022.101140

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Abstract

Abstract Summary Artificial Neural Networks (ANNs) have achieved unequaled performance for numerous problems in many areas of Science, Business, Public Policy, and more. While experts are familiar with performance-oriented software and underlying theory, ANNs are difficult to comprehend for non-experts because it requires skills in programming, background in mathematics and knowledge of terminology and concepts. In this work, we release EpyNN, an educational python resource meant for a public willing to understand key concepts and practical implementation of scalable ANN architectures from concise, homogeneous and idiomatic source code. EpyNN contains an educational Application Programming Interface (API), educational workflows from data preparation to ANN training and a documentation website setting side-by-side code, mathematics, graphical representation and text to facilitate learning and provide teaching material. Overall, EpyNN provides basics for python-fluent individuals who wish to learn, teach or develop from scratch. Availability EpyNN documentation is available at https://epynn.net and repository can be retrieved from https://github.com/synthaze/epynn . Contact Stéphanie Olivier-Van-Stichelen, solivier@mcw.edu . Supplementary Information Supplementary files and listings.

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Research works printed but not linked: 14

  1. Muzio, 2021, Biological network analysis with deep learning, Brief Bioinform, 22, 1515, 10.1093/bib/bbaa257 · DOI
  2. Paszke, 2019, Pytorch: An imperative style, high-performance deep learning library, Adv Neural Inf Process Syst, 32, 8026
  3. Howard, 2020, Fastai: a layered API for deep learning, Information, 11, 108, 10.3390/info11020108 · DOI
  4. Wulff-Fuentes, 2021, The human o-glcnacome database and meta-analysis, Sci Data, 8, 1, 10.1038/s41597-021-00810-4 · DOI
  5. Malard, 2021, Automatization and self-maintenance of the o-glcnacome catalog: a smart scientific database, Database, 2021, 10.1093/database/baab039 · DOI
  6. Van Der Walt, 2011, The numpy array: a structure for efficient numerical computation, Comput Sci Eng, 13, 22, 10.1109/MCSE.2011.37 · DOI · Paper page
  7. Harris, 2020, Array programming with numpy, Nature, 585, 357, 10.1038/s41586-020-2649-2 · DOI · Paper page
  8. Naylor David, Finamore Alessandro, Leontiadis Ilias, Grunenberger Yan, Mellia Marco, Munafò Maurizio, Papagiannaki Konstantina, Steenkiste Peter. The cost of the ”s” in https. In: Proceedings of the 10th ACM international on conference on emerging networking experiments and technologies. 2014, p. 133–40. · DOI
  9. Emmert-Streib, 2020, An introductory review of deep learning for prediction models with big data, Front Artif Intell, 3, 4, 10.3389/frai.2020.00004 · DOI
  10. Sherstinsky, 2020, Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network, Physica D, 404, 10.1016/j.physd.2019.132306 · DOI · Paper page
  11. Sun, 2017, Learning pooling for convolutional neural network, Neurocomputing, 224, 96, 10.1016/j.neucom.2016.10.049 · DOI
  12. Baldi, 2013, Understanding dropout, Adv Neural Inf Process Syst, 26, 2814
  13. Potdar, 2017, A comparative study of categorical variable encoding techniques for neural network classifiers, Int J Comput Appl, 175, 7
  14. Amari, 1993, Backpropagation and stochastic gradient descent method, Neurocomputing, 5, 185, 10.1016/0925-2312(93)90006-O · DOI

Metadata

Authors
Malard, F., Danner, L., Rouzies, E., Meyer, J. G., Lescop, E., & Olivier-Van Stichelen, S.
Affiliations
  • Medical College of Wisconsin (US)
  • Centre National de la Recherche Scientifique (FR)
ORCID
Venue
SoftwareX
Publisher
Elsevier BV
Published
2022-07
Volume
19
Pages
101140
ISSN
2352-7110 (ISSN-L)
DOI
10.1016/j.softx.2022.101140
OpenAlex
W4284706491
Cited by
2
References
4
Citations by year
2025: 1 · 2024: 1
Type
software paper
Language
en
Open access
Licence
Locations
  • SoftwareX (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • HAL (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Medical College of Wisconsin
  • Eidgenössische Technische Hochschule Zürich
  • National Institutes of Health (R00 HD087430)
Topics
  • Social Sciences › Education › Undergraduate Neuroscience Education and Research
  • Physical Sciences › Computer Science › Software › Computational Physics and Python Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords
artificial neural networks, educational software, Python, neural network implementation, API design, data preparation, machine learning education, teaching materials

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