Difference between revisions of "Reviews"
From Research management course
m |
|||
| Line 1: | Line 1: | ||
| − | =Encyclopediae= | + | {{#seo: |
| + | |title=Course My first scientific article: Reviews | ||
| + | |titlemode=replace | ||
| + | |keywords=scientific article | ||
| + | |description=Course Writing a scientific article: A step-by-step guide for students. | ||
| + | }} | ||
| + | |||
| + | ==Encyclopediae== | ||
*[https://mathworld.wolfram.com MathWorld Encyclopedia by wolfram] | *[https://mathworld.wolfram.com MathWorld Encyclopedia by wolfram] | ||
*[https://www.encyclopediaofmath.org Encyclopedia of Mathematics wiki by Kluwer Academic Publishers, 2002] | *[https://www.encyclopediaofmath.org Encyclopedia of Mathematics wiki by Kluwer Academic Publishers, 2002] | ||
| Line 7: | Line 14: | ||
*[https://arxiv.org/abs/1911.00892v1 Boosting Vector Calculus with the Graphical Notation by Joon-Hwi Kim et al., 2019] | *[https://arxiv.org/abs/1911.00892v1 Boosting Vector Calculus with the Graphical Notation by Joon-Hwi Kim et al., 2019] | ||
| − | =Functional data analysis= | + | ==Functional data analysis== |
*[https://www.math3ma.com/ Math3ma by Thai-Danae Bradley, 2020] | *[https://www.math3ma.com/ Math3ma by Thai-Danae Bradley, 2020] | ||
*[https://arxiv.org/abs/2004.05631 At the Interface of Algebra and Statistics by Tai-Danae Bradley, 2020] (category theory) | *[https://arxiv.org/abs/2004.05631 At the Interface of Algebra and Statistics by Tai-Danae Bradley, 2020] (category theory) | ||
* [https://arxiv.org/pdf/2006.07360.pdf AlgebraNets by Jordan Hoffmann et al., 2020] | * [https://arxiv.org/pdf/2006.07360.pdf AlgebraNets by Jordan Hoffmann et al., 2020] | ||
| − | =Statistics= | + | ==Statistics== |
*[https://medium.com/@ciortanmadalina/overview-of-data-distributions-87d95a5cbf0a Overview of data distributions] | *[https://medium.com/@ciortanmadalina/overview-of-data-distributions-87d95a5cbf0a Overview of data distributions] | ||
* [https://projecteuclid.org/download/pdf_1/euclid.aos/1013699998 The control of the false discovery rate in multiple testing under dependency by Yoav Benjamini and Daniel Yekutieli, 2001] | * [https://projecteuclid.org/download/pdf_1/euclid.aos/1013699998 The control of the false discovery rate in multiple testing under dependency by Yoav Benjamini and Daniel Yekutieli, 2001] | ||
* [http://www3.stat.sinica.edu.tw/statistica/oldpdf/A13n212.pdf Conjugate Priors for Generalized Linear Models by Ming-Hui Chen and Joseph G. Ibrahim, 2003] | * [http://www3.stat.sinica.edu.tw/statistica/oldpdf/A13n212.pdf Conjugate Priors for Generalized Linear Models by Ming-Hui Chen and Joseph G. Ibrahim, 2003] | ||
| − | =Programming= | + | ==Programming== |
*[Papers with code] | *[Papers with code] | ||
*[https://medium.com/machine-learning-in-practice/over-200-of-the-best-machine-learning-nlp-and-python-tutorials-2018-edition-dd8cf53cb7dc Over 200 of the Best Machine Learning, NLP, and Python Tutorials by Robbie Allen, 2018] | *[https://medium.com/machine-learning-in-practice/over-200-of-the-best-machine-learning-nlp-and-python-tutorials-2018-edition-dd8cf53cb7dc Over 200 of the Best Machine Learning, NLP, and Python Tutorials by Robbie Allen, 2018] | ||
| − | =Technical communications= | + | ==Technical communications== |
*[https://www.nature.com/scitable/ebooks/english-communication-for-scientists-14053993/contents/ English Communication for Scientists by Nature Education, 2014] | *[https://www.nature.com/scitable/ebooks/english-communication-for-scientists-14053993/contents/ English Communication for Scientists by Nature Education, 2014] | ||
*[https://www.sciencedirect.com/science/article/abs/pii/S1878764915001606 <del>Writing a scientific article: A step-by-step guide for beginners</del> by F.Ecarnot et al., 2015 in European Geriatric <b>Medicine</b>] | *[https://www.sciencedirect.com/science/article/abs/pii/S1878764915001606 <del>Writing a scientific article: A step-by-step guide for beginners</del> by F.Ecarnot et al., 2015 in European Geriatric <b>Medicine</b>] | ||
| Line 27: | Line 34: | ||
*[https://spie.org/samples/9781510619142.pdf How to Write a GoodScientific Paper by Chris A. Mack, 2018 in SPIE.org] | *[https://spie.org/samples/9781510619142.pdf How to Write a GoodScientific Paper by Chris A. Mack, 2018 in SPIE.org] | ||
| − | =Unsorted= | + | ==Unsorted== |
*[https://github.com/mukeshmithrakumar/Book_List Python, Machine Learning, Deep Learning and Data Science Books] | *[https://github.com/mukeshmithrakumar/Book_List Python, Machine Learning, Deep Learning and Data Science Books] | ||
| − | =News, sensationalism= | + | ==News, sensationalism== |
| − | *[https://drive.google.com/file/d/10rZ4ldHUBoDu8bVoqGn-CEqGgX-w-oKm/view?fbclid=IwAR0FKijayJz4QO3IeC6_D7766TRMEwIDPlJp_JzaoSV5Bh7e7OxJ7Xukb2I Deep learning: | + | *[https://drive.google.com/file/d/10rZ4ldHUBoDu8bVoqGn-CEqGgX-w-oKm/view?fbclid=IwAR0FKijayJz4QO3IeC6_D7766TRMEwIDPlJp_JzaoSV5Bh7e7OxJ7Xukb2I Deep learning: state of the art by Lex Fridman, MIT, 2020] |
*[https://towardsdatascience.com/when-bayes-ockham-and-shannon-come-together-to-define-machine-learning-96422729a1ad When Bayes, Ockham, and Shannon come together to define machine learning by Tirthajyoti Sarkar, 2018] | *[https://towardsdatascience.com/when-bayes-ockham-and-shannon-come-together-to-define-machine-learning-96422729a1ad When Bayes, Ockham, and Shannon come together to define machine learning by Tirthajyoti Sarkar, 2018] | ||
*[https://www.analyticsvidhya.com/blog/2017/12/introduction-to-recurrent-neural-networks/?fbclid=IwAR0s_QEvbVupVO-OAFwLOvWQPynUc70FCIxV-XEm4y0Md-0RWRLv18r8-Mc Introduction to Recurrent Neural Networks by Dishashree Gupta, 2017] | *[https://www.analyticsvidhya.com/blog/2017/12/introduction-to-recurrent-neural-networks/?fbclid=IwAR0s_QEvbVupVO-OAFwLOvWQPynUc70FCIxV-XEm4y0Md-0RWRLv18r8-Mc Introduction to Recurrent Neural Networks by Dishashree Gupta, 2017] | ||
*[https://medium.com/@jehillparikh/bayes-by-back-prop-bbb-from-robust-neural-networks-to-unified-theory-of-brain-788fd83f0e38 Bayes by Backprop (BBB): Robust Neural Networks to Unified theory of Brain? by Jehill Parikh, 2019] | *[https://medium.com/@jehillparikh/bayes-by-back-prop-bbb-from-robust-neural-networks-to-unified-theory-of-brain-788fd83f0e38 Bayes by Backprop (BBB): Robust Neural Networks to Unified theory of Brain? by Jehill Parikh, 2019] | ||
Revision as of 13:13, 17 February 2024
Contents
Encyclopediae
- MathWorld Encyclopedia by wolfram
- Encyclopedia of Mathematics wiki by Kluwer Academic Publishers, 2002
- Introduction to Probabilistic Programming by Ayan Das, 2020
Mathematics for machine learning
Functional data analysis
- Math3ma by Thai-Danae Bradley, 2020
- At the Interface of Algebra and Statistics by Tai-Danae Bradley, 2020 (category theory)
- AlgebraNets by Jordan Hoffmann et al., 2020
Statistics
- Overview of data distributions
- The control of the false discovery rate in multiple testing under dependency by Yoav Benjamini and Daniel Yekutieli, 2001
- Conjugate Priors for Generalized Linear Models by Ming-Hui Chen and Joseph G. Ibrahim, 2003
Programming
- [Papers with code]
- Over 200 of the Best Machine Learning, NLP, and Python Tutorials by Robbie Allen, 2018
Technical communications
- English Communication for Scientists by Nature Education, 2014
Writing a scientific article: A step-by-step guide for beginnersby F.Ecarnot et al., 2015 in European Geriatric Medicine- 11 steps to structuring a science paper editors will take seriously by By Angel Borja, 2014 in Elsevier connections
- How to Write a GoodScientific Paper by Chris A. Mack, 2018 in SPIE.org
Unsorted
News, sensationalism
- Deep learning: state of the art by Lex Fridman, MIT, 2020
- When Bayes, Ockham, and Shannon come together to define machine learning by Tirthajyoti Sarkar, 2018
- Introduction to Recurrent Neural Networks by Dishashree Gupta, 2017
- Bayes by Backprop (BBB): Robust Neural Networks to Unified theory of Brain? by Jehill Parikh, 2019