👏 Predicting whether a developer uses R or Python
Myself being an avid Python user, I thought it'd be fun to see if based on this survey I could predict whether a given developer uses R or Python - and of course if so, which features allow the classifier to determine that. I'll try to keep the analysis as simple as possible and focus on clarity of code and analysis rather than on creating anything overly complex and detailed.
🔹 Conclusions?
If you do not want to go through the notebook, the quick conclusion is that among data scientists and analysts, Python and R users are pretty similar. It is however possible to create pretty decent classifiers for predicting whether a user uses R or Python, and there are a few funny conclusions and reasonings to be found within those classifiers.
https://www.kaggle.com/nanomathias/predicting-r-vs-python?utm_medium=social&utm_source=twitter.com&utm_campaign=Weekly-Kernel-Awards
Myself being an avid Python user, I thought it'd be fun to see if based on this survey I could predict whether a given developer uses R or Python - and of course if so, which features allow the classifier to determine that. I'll try to keep the analysis as simple as possible and focus on clarity of code and analysis rather than on creating anything overly complex and detailed.
🔹 Conclusions?
If you do not want to go through the notebook, the quick conclusion is that among data scientists and analysts, Python and R users are pretty similar. It is however possible to create pretty decent classifiers for predicting whether a user uses R or Python, and there are a few funny conclusions and reasonings to be found within those classifiers.
https://www.kaggle.com/nanomathias/predicting-r-vs-python?utm_medium=social&utm_source=twitter.com&utm_campaign=Weekly-Kernel-Awards
💰 Lots of #postdoc positions get posted on this Soft Matter mailing list: https://t.co/CmuJpKMAtF
www1.maths.leeds.ac.uk
The Soft Matter Mailing List
Soft Matter Mailing List Information
🔹 هاروارد کورسی گذاشته توی edX لینوکس و گیت و ... درس میده:
https://www.edx.org/course/data-science-productivity-tools-harvardx-ph125-5x?utm_source=twitter&utm_medium=social&utm_campaign=pc%2Ccourse%2Charvardx%2Cdata-science-productivity-tools%2Cjune92018
به نظر کورس مهمی هست و جاش خالی بود.
خیلیا کد بلدن بنویسن، ولی همهی پروژهشون شلختهس چون نمیدونن چه ابزارهایی برای مرتب نگه داشتنش هست.
https://www.edx.org/course/data-science-productivity-tools-harvardx-ph125-5x?utm_source=twitter&utm_medium=social&utm_campaign=pc%2Ccourse%2Charvardx%2Cdata-science-productivity-tools%2Cjune92018
به نظر کورس مهمی هست و جاش خالی بود.
خیلیا کد بلدن بنویسن، ولی همهی پروژهشون شلختهس چون نمیدونن چه ابزارهایی برای مرتب نگه داشتنش هست.
edX
Data Science: Productivity Tools
Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.
💰 Open #Postdoc position in the field of Granular Matter Physics offered at the MPI for Dynamics and Self-Organization https://t.co/cg8EQrxrlI #PostdocGermany
www.mpg.de
Postdoc position - Granular Matter, Soft Matter Physics
We seek highly motivated, outstanding candidates interested in questions about the research topics “granular matter” and “numerical simulations”.
Within the framework of research program funded by a third party, we have investigated the impact of geometry…
Within the framework of research program funded by a third party, we have investigated the impact of geometry…
2018 Fellows of the Network Science Society announced at #netsci2018.
🔖 The importance of the whole: topological data analysis for the network neuroscientist
Ann E. Sizemore, Jennifer Phillips-Cremins, Robert Ghrist, Danielle S. Bassett
🔗 https://arxiv.org/pdf/1806.05167
📌 ABSTRACT
The application of network techniques to the analysis of neural data has greatly improved our ability to quantify and describe these rich interacting systems. Among many important contributions, networks have proven useful in identifying sets of node pairs that are densely connected and that collectively support brain function. Yet the restriction to pairwise interactions prevents us from realizing intrinsic topological features such as cavities within the interconnection structure that may be just as crucial for proper function. To detect and quantify these topological features we must turn to methods from algebraic topology that encode data as a simplicial complex built of sets of interacting nodes called simplices. On this substrate, we can then use the relations between simplices and higher-order connectivity to expose cavities within the complex, thereby summarizing its topological nature. Here we provide an introduction to persistent homology, a fundamental method from applied topology that builds a global denoscriptor of system structure by chronicling the evolution of cavities as we move through a combinatorial object such as a weighted network. We detail the underlying mathematics and perform demonstrative calculations on the mouse structural connectome, electrical and chemical synapses in \textit{C. elegans}, and genomic interaction data. Finally we suggest avenues for future work and highlight new advances in mathematics that appear ready for use in revealing the architecture and function of neural systems.
Ann E. Sizemore, Jennifer Phillips-Cremins, Robert Ghrist, Danielle S. Bassett
🔗 https://arxiv.org/pdf/1806.05167
📌 ABSTRACT
The application of network techniques to the analysis of neural data has greatly improved our ability to quantify and describe these rich interacting systems. Among many important contributions, networks have proven useful in identifying sets of node pairs that are densely connected and that collectively support brain function. Yet the restriction to pairwise interactions prevents us from realizing intrinsic topological features such as cavities within the interconnection structure that may be just as crucial for proper function. To detect and quantify these topological features we must turn to methods from algebraic topology that encode data as a simplicial complex built of sets of interacting nodes called simplices. On this substrate, we can then use the relations between simplices and higher-order connectivity to expose cavities within the complex, thereby summarizing its topological nature. Here we provide an introduction to persistent homology, a fundamental method from applied topology that builds a global denoscriptor of system structure by chronicling the evolution of cavities as we move through a combinatorial object such as a weighted network. We detail the underlying mathematics and perform demonstrative calculations on the mouse structural connectome, electrical and chemical synapses in \textit{C. elegans}, and genomic interaction data. Finally we suggest avenues for future work and highlight new advances in mathematics that appear ready for use in revealing the architecture and function of neural systems.
"The importance of the whole: topological data analysis for the network neuroscientist"
https://arxiv.org/pdf/1806.05167
https://arxiv.org/pdf/1806.05167
🌋http://www.quantamagazine.org/the-physics-of-glass-opens-a-window-into-biology-20180611/
In glassy systems, we think that many of these interesting properties occur because there’s what’s called a complex potential energy landscape. If you consider the total energy of the entire system as a function of where the atoms are, then in a glass, which is disordered, that landscape is incredibly complex.
It turns out that the neural networks used for deep learning and optimization share a surprisingly large number of properties with glasses. You can think of the nodes of the network as particles, and the connections between them as the bonds between particles. If you do, the neural networks and the glasses have complex potential energy landscapes with nearly identical properties. For example, questions about the energy barriers between states in a neural network are related to questions about how likely it is for a glassy material to flow. So the hope is that understanding some of the properties of glasses can help you understand optimization in these neural networks, too.
In glassy systems, we think that many of these interesting properties occur because there’s what’s called a complex potential energy landscape. If you consider the total energy of the entire system as a function of where the atoms are, then in a glass, which is disordered, that landscape is incredibly complex.
It turns out that the neural networks used for deep learning and optimization share a surprisingly large number of properties with glasses. You can think of the nodes of the network as particles, and the connections between them as the bonds between particles. If you do, the neural networks and the glasses have complex potential energy landscapes with nearly identical properties. For example, questions about the energy barriers between states in a neural network are related to questions about how likely it is for a glassy material to flow. So the hope is that understanding some of the properties of glasses can help you understand optimization in these neural networks, too.
Quanta Magazine
The Physics of Glass Opens a Window Into Biology
The physicist Lisa Manning studies the dynamics of glassy materials to understand embryonic development and disease.
NEW M.Sc. Program in Natural Language Processing (NLP) and Data Science
Université de Lorraine, Nancy (France)
The Institute of Digital science, Management and Cognition
is opening a a new Masters Program in NLP -
Computer Sciences, Speech, Language and Knowledge Representation
**********
http://institut-sciences-digitales.fr/idmc-master-degree-in-natural-language-processing/
**********
So you want to be a specialist in Neural Networks, Logic, Speech
Processing, Information Retrieval, Knowledge Representation ... all
for Natural Language? Well, now's your chance!
Natural Language Processing (NLP) lies at the crossroads of
linguistics, computer science and artificial intelligence. This
Masters Program offers
a modern curriculum which combines the different
approaches, and covers both theoretical and applied perspectives.
In each semester, the program includes a hands-on project.
It ends with a 6-month paid internship in a company or a research
lab. You can find the course denoscription below.
----------------------------------------------------------------------------------------
You can apply to directly enter at either the first year or second year level.
----------------------------------------------------------------------------------------
Language
-------------
All courses are taught in English
(except the "French for non-native Speakers" class).
Fees
-------
The University of Lorraine is publicly funded and thus offers
tuition-free education for all students including students from
both inside and outside EU/EEA/EFTA countries. The only student
expenditures are a nominal semester fee of about 600 euros which
includes health insurance. Nancy’s high quality of life goes
hand-in-hand with a low cost of living.
Université de Lorraine, Nancy (France)
The Institute of Digital science, Management and Cognition
is opening a a new Masters Program in NLP -
Computer Sciences, Speech, Language and Knowledge Representation
**********
http://institut-sciences-digitales.fr/idmc-master-degree-in-natural-language-processing/
**********
So you want to be a specialist in Neural Networks, Logic, Speech
Processing, Information Retrieval, Knowledge Representation ... all
for Natural Language? Well, now's your chance!
Natural Language Processing (NLP) lies at the crossroads of
linguistics, computer science and artificial intelligence. This
Masters Program offers
a modern curriculum which combines the different
approaches, and covers both theoretical and applied perspectives.
In each semester, the program includes a hands-on project.
It ends with a 6-month paid internship in a company or a research
lab. You can find the course denoscription below.
----------------------------------------------------------------------------------------
You can apply to directly enter at either the first year or second year level.
----------------------------------------------------------------------------------------
Language
-------------
All courses are taught in English
(except the "French for non-native Speakers" class).
Fees
-------
The University of Lorraine is publicly funded and thus offers
tuition-free education for all students including students from
both inside and outside EU/EEA/EFTA countries. The only student
expenditures are a nominal semester fee of about 600 euros which
includes health insurance. Nancy’s high quality of life goes
hand-in-hand with a low cost of living.
An interdisciplinary forum for complexity research :
PLOS Complexity Channel
https://t.co/Z6X8qNEuN2
Meet the editor here
https://t.co/slUfig9dUC https://t.co/0eiYrC40O9
PLOS Complexity Channel
https://t.co/Z6X8qNEuN2
Meet the editor here
https://t.co/slUfig9dUC https://t.co/0eiYrC40O9
🌀 “How to build your own Neural Network from scratch in Python” by James Loy
https://towardsdatascience.com/how-to-build-your-own-neural-network-from-scratch-in-python-68998a08e4f6
https://towardsdatascience.com/how-to-build-your-own-neural-network-from-scratch-in-python-68998a08e4f6
Towards Data Science
How to build a neural network from zero | Towards Data Science
No frameworks, just Python