Difference between revisions of "Reviews"

From Research management course
Jump to: navigation, search
 
(7 intermediate revisions by one other user not shown)
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]
 
*[https://dasayan05.github.io/blog-tut/2020/05/05/probabilistic-programming.html?fbclid=IwAR1WBNV3HPQHw5QVJc9esfIgNII1jT8RxSRMnfFigpTjehwObSQL5SUH1HA Introduction to Probabilistic Programming by Ayan Das, 2020]
 
*[https://dasayan05.github.io/blog-tut/2020/05/05/probabilistic-programming.html?fbclid=IwAR1WBNV3HPQHw5QVJc9esfIgNII1jT8RxSRMnfFigpTjehwObSQL5SUH1HA Introduction to Probabilistic Programming by Ayan Das, 2020]
  
=Functional data analysis=
+
=Mathematics for machine learning=
 +
*[https://arxiv.org/abs/1911.00892v1 Boosting Vector Calculus with the Graphical Notation by Joon-Hwi Kim et al., 2019]
 +
 
 +
==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]
  
=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]
 +
* [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==
 +
*[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.elsevier.com/connect/11-steps-to-structuring-a-science-paper-editors-will-take-seriously 11 steps to structuring a science paper editors will take seriously by By Angel Borja, 2014 in Elsevier connections]
 +
*[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]
  
=Reviews for teenagers=
+
==News, sensationalism==
 +
*[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?
+
*[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]
by Jehill Parikh, 2019]
 
 
 
=News, sensationalism=
 
*[https://drive.google.com/file/d/10rZ4ldHUBoDu8bVoqGn-CEqGgX-w-oKm/view?fbclid=IwAR0FKijayJz4QO3IeC6_D7766TRMEwIDPlJp_JzaoSV5Bh7e7OxJ7Xukb2I Deep learning: sate of the art by Lex Fridman, MIT, 2020]
 

Latest revision as of 13:13, 17 February 2024

Encyclopediae

Mathematics for machine learning

Functional data analysis

Statistics

Programming

Technical communications

Unsorted

News, sensationalism