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EpyNN: Educational python for Neural Networks
Open accessPreprint — published as 10.1016/j.softx.2022.101140 · Paper page
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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Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
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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)
- Venue
- SoftwareX
- Publisher
- openRxiv
- Published
- 2021-12-07
- Volume
- 19
- Pages
- 101140-101140
- ISSN
- 2352-7110 (ISSN-L)
- OpenAlex
- W4284706491
- Cited by
- 2
- References
- 1
- Citations by year
- 2025: 1 · 2024: 1
- Type
- software paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- 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
- Agence Nationale de la Recherche (ANR-10-LABX-0033)
- Eidgenössische Technische Hochschule Zürich
- National Institutes of Health
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R00HD087430)
- 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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