🌀 Neural Networks for Machine Learning
About this course: Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well. This course contains the same content presented on Coursera beginning in 2013. It is not a continuation or update of the original course. It has been adapted for the new platform. Please be advised that the course is suited for an intermediate level learner - comfortable with calculus and with experience programming (Python).
https://www.coursera.org/learn/neural-networks?recoOrder=16&utm_medium=email&utm_source=recommendations&utm_campaign=recommendationsEmail%7Erecs_email_2016_11_20_17%3A58
About this course: Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well. This course contains the same content presented on Coursera beginning in 2013. It is not a continuation or update of the original course. It has been adapted for the new platform. Please be advised that the course is suited for an intermediate level learner - comfortable with calculus and with experience programming (Python).
https://www.coursera.org/learn/neural-networks?recoOrder=16&utm_medium=email&utm_source=recommendations&utm_campaign=recommendationsEmail%7Erecs_email_2016_11_20_17%3A58
🌀 Introduction to Data Science in Python
About this course: This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis. The course will end with a statistics primer, showing how various statistical measures can be applied to pandas DataFrames. By the end of the course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course is number 1 in the Applied Data Science with Python specialization and should be taken before any other courses in the specialization.
https://www.coursera.org/learn/python-data-analysis?recoOrder=4&utm_medium=email&utm_source=recommendations&utm_campaign=recommendationsEmail~recs_email_2016_11_20_17%3A58
About this course: This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis. The course will end with a statistics primer, showing how various statistical measures can be applied to pandas DataFrames. By the end of the course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course is number 1 in the Applied Data Science with Python specialization and should be taken before any other courses in the specialization.
https://www.coursera.org/learn/python-data-analysis?recoOrder=4&utm_medium=email&utm_source=recommendations&utm_campaign=recommendationsEmail~recs_email_2016_11_20_17%3A58
Coursera
Introduction to Data Science in Python | Coursera
Learn Introduction to Data Science in Python from ...
📄 Big data need physical ideas and methods
https://arxiv.org/pdf/1412.6848v1
📌 If a person looks at WHITE paper through BLUE glasses, the paper will become BLUE in the eye of the person. Likewise, in the current study of big data which play the same role as the white paper being looked at, various statistical methods just serve as the blue glasses. That is, results obtained from big data often depend on the statistical methods in use, which may often defy reality. Here I suggest using physical ideas and methods to overcome this problem to the greatest extent. This suggestion is helpful to development and application of big data.
#Data_Analysis , #Statistics and #Probability (physics.data-an)
https://arxiv.org/pdf/1412.6848v1
📌 If a person looks at WHITE paper through BLUE glasses, the paper will become BLUE in the eye of the person. Likewise, in the current study of big data which play the same role as the white paper being looked at, various statistical methods just serve as the blue glasses. That is, results obtained from big data often depend on the statistical methods in use, which may often defy reality. Here I suggest using physical ideas and methods to overcome this problem to the greatest extent. This suggestion is helpful to development and application of big data.
#Data_Analysis , #Statistics and #Probability (physics.data-an)
🎯 A new quantum approach to big data
System for handling massive digital datasets could make impossibly complex problems solvable.
http://news.mit.edu/2016/quantum-approach-big-data-0125
System for handling massive digital datasets could make impossibly complex problems solvable.
http://news.mit.edu/2016/quantum-approach-big-data-0125
MIT News
A new quantum approach to big data
A new quantum computing system for handling massive digital datasets could make impossibly complex problems solvable.
📄 Thermodynamics of information
Juan M. R. Parrondo, Jordan M. Horowitz & Takahiro Sagawa
http://www.nature.com/nphys/journal/v11/n2/abs/nphys3230.html
📌 Abstract
By its very nature, the second law of thermodynamics is probabilistic, in that its formulation requires a probabilistic description of the state of a system. This raises questions about the objectivity of the second law: does it depend, for example, on what we know about the system? For over a century, much effort has been devoted to incorporating information into thermodynamics and assessing the entropic and energetic costs of manipulating information. More recently, this historically theoretical pursuit has become relevant in practical situations where information is manipulated at small scales, such as in molecular and cell biology, artificial nano-devices or quantum computation. Here we give an introduction to a novel theoretical framework for the thermodynamics of information based on stochastic thermodynamics and fluctuation theorems, review some recent experimental results, and present an overview of the state of the art in the field.
Juan M. R. Parrondo, Jordan M. Horowitz & Takahiro Sagawa
http://www.nature.com/nphys/journal/v11/n2/abs/nphys3230.html
📌 Abstract
By its very nature, the second law of thermodynamics is probabilistic, in that its formulation requires a probabilistic description of the state of a system. This raises questions about the objectivity of the second law: does it depend, for example, on what we know about the system? For over a century, much effort has been devoted to incorporating information into thermodynamics and assessing the entropic and energetic costs of manipulating information. More recently, this historically theoretical pursuit has become relevant in practical situations where information is manipulated at small scales, such as in molecular and cell biology, artificial nano-devices or quantum computation. Here we give an introduction to a novel theoretical framework for the thermodynamics of information based on stochastic thermodynamics and fluctuation theorems, review some recent experimental results, and present an overview of the state of the art in the field.
Nature
Thermodynamics of information : Nature Physics : Nature Research
The task of integrating information into the framework of thermodynamics dates back to Maxwell and his infamous demon. Recent advances have made these ideas rigorous[mdash]and brought them into the laboratory.
🔵 New Python library to download &analyze street networks from #OpenStreetMap
#Python
http://urbandemographics.blogspot.co.uk/2016/11/python-links.html?m=1
#Python
http://urbandemographics.blogspot.co.uk/2016/11/python-links.html?m=1
urbandemographics.blogspot.co.uk
Python Links
An overview of data science in Python PySAL: a Python library for spatial analysis , great collaborative by Serge Rey and Dani Arribas...
📝 The many facets of community detection in complex networks
Michael T. Schaub, Jean-Charles Delvenne, Martin Rosvall, Renaud Lambiotte
https://arxiv.org/pdf/1611.07769v1
📌 ABSTRACT
Community detection, the decomposition of a graph into meaningful building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of community structure, and classified based on the mathematical techniques they employ. However, this can be misleading because apparent similarities in their mathematical machinery can disguise entirely different objectives. Here we provide a focused review of the different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines of research, and points out open directions and avenues for future research.
#Social and #Information #Networks (cs.SI); #Data_Analysis, #Statistics and #Probability (physics.data-an); #Physics and #Society (physics.soc-ph
Michael T. Schaub, Jean-Charles Delvenne, Martin Rosvall, Renaud Lambiotte
https://arxiv.org/pdf/1611.07769v1
📌 ABSTRACT
Community detection, the decomposition of a graph into meaningful building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of community structure, and classified based on the mathematical techniques they employ. However, this can be misleading because apparent similarities in their mathematical machinery can disguise entirely different objectives. Here we provide a focused review of the different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines of research, and points out open directions and avenues for future research.
#Social and #Information #Networks (cs.SI); #Data_Analysis, #Statistics and #Probability (physics.data-an); #Physics and #Society (physics.soc-ph
📽 Nonequilibrium Statistical Mechanics
Chris Jarzynski
University of Maryland
1⃣ Fundamental Problems and Applications Nonequilibrium work relations
https://www.youtube.com/watch?v=LXcQx6Bu3OQ
2⃣ Microscopic systems driven away from equilibrium
https://www.youtube.com/watch?v=Epud4i_Y5KM
3⃣ Dissipation and the Arrow of Time
https://www.youtube.com/watch?v=anG9DlF-EgY
Chris Jarzynski
University of Maryland
1⃣ Fundamental Problems and Applications Nonequilibrium work relations
https://www.youtube.com/watch?v=LXcQx6Bu3OQ
2⃣ Microscopic systems driven away from equilibrium
https://www.youtube.com/watch?v=Epud4i_Y5KM
3⃣ Dissipation and the Arrow of Time
https://www.youtube.com/watch?v=anG9DlF-EgY
YouTube
Nonequilibrium Statistical Mechanics I - Chris Jarzynski
Lecture 1 of 3 in Series
Fundamental Problems and Applications Nonequilibrium work relations - Chris Jarzynski, University of Maryland
Hits from scivee.tv prior to youtube upload : 2113
Fundamental Problems and Applications Nonequilibrium work relations - Chris Jarzynski, University of Maryland
Hits from scivee.tv prior to youtube upload : 2113
📽 Emergent Order in Biology
https://www.youtube.com/playlist?list=PLdV51-1AzT3OVG3n9TG5s-hGkXMhcgLji
https://www.youtube.com/playlist?list=PLdV51-1AzT3OVG3n9TG5s-hGkXMhcgLji
📌 Equalities and Inequalities:
Irreversibility and the Second Law of Thermodynamics at the Nanoscale
http://www.chem.umd.edu/wp-content/uploads/2012/12/Jarzynski_AnnuRevCondMattPhys_2_329_20111.pdf
Christopher Jarzynski
Irreversibility and the Second Law of Thermodynamics at the Nanoscale
http://www.chem.umd.edu/wp-content/uploads/2012/12/Jarzynski_AnnuRevCondMattPhys_2_329_20111.pdf
Christopher Jarzynski
🌀 Are we living in the #matrix ? Great interview with network scientist Dmitri Krioukov
http://www.northeastern.edu/news/2016/11/3qs-are-we-living-in-a-matrix-style-simulation/
http://www.northeastern.edu/news/2016/11/3qs-are-we-living-in-a-matrix-style-simulation/