際際滷shows by User: DmitryZinoviev / http://www.slideshare.net/images/logo.gif 際際滷shows by User: DmitryZinoviev / Tue, 08 Oct 2024 02:41:30 GMT 際際滷Share feed for 際際滷shows by User: DmitryZinoviev What You Can Learn from Obscure Programming Languages /slideshow/what-you-can-learn-from-obscure-programming-languages/272254040 codebar-2024-241008024130-72f4a1c4
A quick overview of several "osbcure" programming languages: Occam, Forth, APL, and Simula.]]>
A quick overview of several "osbcure" programming languages: Occam, Forth, APL, and Simula.]]> Tue, 08 Oct 2024 02:41:30 GMT /slideshow/what-you-can-learn-from-obscure-programming-languages/272254040 DmitryZinoviev@slideshare.net(DmitryZinoviev) What You Can Learn from Obscure Programming Languages DmitryZinoviev A quick overview of several "osbcure" programming languages: Occam, Forth, APL, and Simula. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/codebar-2024-241008024130-72f4a1c4-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A quick overview of several &quot;osbcure&quot; programming languages: Occam, Forth, APL, and Simula.
What You Can Learn from Obscure Programming Languages from Dmitry Zinoviev
]]> 135 0 https://cdn.slidesharecdn.com/ss_thumbnails/codebar-2024-241008024130-72f4a1c4-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Machine Learning Basics for Dummies (no math!) /slideshow/machine-learning-basics-for-dummies-no-math/266492664 02-basics-240226070604-98eb3fc7
際際滷s to the third lecture of my undergraduate course "AI & Society."]]>
際際滷s to the third lecture of my undergraduate course "AI & Society."]]> Mon, 26 Feb 2024 07:06:04 GMT /slideshow/machine-learning-basics-for-dummies-no-math/266492664 DmitryZinoviev@slideshare.net(DmitryZinoviev) Machine Learning Basics for Dummies (no math!) DmitryZinoviev 際際滷s to the third lecture of my undergraduate course "AI & Society." <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/02-basics-240226070604-98eb3fc7-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 際際滷s to the third lecture of my undergraduate course &quot;AI &amp; Society.&quot;
Machine Learning Basics for Dummies (no math!) from Dmitry Zinoviev
]]> 148 0 https://cdn.slidesharecdn.com/ss_thumbnails/02-basics-240226070604-98eb3fc7-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 WHat is star discourse in post-Soviet film journals? /slideshow/what-is-star-discourse-in-postsoviet-film-journals/264675505 cudan-2023-mukhortova-231215150908-ef40d88d
Presentation at CUDAN-2023.]]>
Presentation at CUDAN-2023.]]> Fri, 15 Dec 2023 15:09:08 GMT /slideshow/what-is-star-discourse-in-postsoviet-film-journals/264675505 DmitryZinoviev@slideshare.net(DmitryZinoviev) WHat is star discourse in post-Soviet film journals? DmitryZinoviev Presentation at CUDAN-2023. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cudan-2023-mukhortova-231215150908-ef40d88d-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presentation at CUDAN-2023.
WHat is star discourse in post-Soviet film journals? from Dmitry Zinoviev
]]> 30 0 https://cdn.slidesharecdn.com/ss_thumbnails/cudan-2023-mukhortova-231215150908-ef40d88d-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 The Musk Effect at Twitter /slideshow/the-musk-effect-at-twitter/258728664 presentation-230629231453-3b20bb15
Presentation at SunBelt 2023]]>
Presentation at SunBelt 2023]]> Thu, 29 Jun 2023 23:14:53 GMT /slideshow/the-musk-effect-at-twitter/258728664 DmitryZinoviev@slideshare.net(DmitryZinoviev) The Musk Effect at Twitter DmitryZinoviev Presentation at SunBelt 2023 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-230629231453-3b20bb15-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presentation at SunBelt 2023
The Musk Effect at Twitter from Dmitry Zinoviev
]]> 76 0 https://cdn.slidesharecdn.com/ss_thumbnails/presentation-230629231453-3b20bb15-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Are Twitter Networks of Regional Entrepreneurs Gendered? /DmitryZinoviev/are-twitter-networks-of-regional-entrepreneurs-gendered networks-entrepreneurs-2022-220714222558-62618bab
Presentation at SunBelt-2022]]>
Presentation at SunBelt-2022]]> Thu, 14 Jul 2022 22:25:57 GMT /DmitryZinoviev/are-twitter-networks-of-regional-entrepreneurs-gendered DmitryZinoviev@slideshare.net(DmitryZinoviev) Are Twitter Networks of Regional Entrepreneurs Gendered? DmitryZinoviev Presentation at SunBelt-2022 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/networks-entrepreneurs-2022-220714222558-62618bab-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presentation at SunBelt-2022
Are Twitter Networks of Regional Entrepreneurs Gendered? from Dmitry Zinoviev
]]> 25 0 https://cdn.slidesharecdn.com/ss_thumbnails/networks-entrepreneurs-2022-220714222558-62618bab-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Using Complex Network Analysis for Periodization /slideshow/using-complex-network-analysis-for-periodization/252003487 pacss-2022-220617044442-723d472e
Periodization is the process of categorizing the past into discrete, contiguous, quantified, and named blocks of time and the results of such a process. I propose to model historical states as complex networks and use complex network analysis (CNA) for periodization.]]>
Periodization is the process of categorizing the past into discrete, contiguous, quantified, and named blocks of time and the results of such a process. I propose to model historical states as complex networks and use complex network analysis (CNA) for periodization.]]> Fri, 17 Jun 2022 04:44:42 GMT /slideshow/using-complex-network-analysis-for-periodization/252003487 DmitryZinoviev@slideshare.net(DmitryZinoviev) Using Complex Network Analysis for Periodization DmitryZinoviev Periodization is the process of categorizing the past into discrete, contiguous, quantified, and named blocks of time and the results of such a process. I propose to model historical states as complex networks and use complex network analysis (CNA) for periodization. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pacss-2022-220617044442-723d472e-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Periodization is the process of categorizing the past into discrete, contiguous, quantified, and named blocks of time and the results of such a process. I propose to model historical states as complex networks and use complex network analysis (CNA) for periodization.
Using Complex Network Analysis for Periodization from Dmitry Zinoviev
]]> 46 0 https://cdn.slidesharecdn.com/ss_thumbnails/pacss-2022-220617044442-723d472e-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Algorithms /slideshow/algorithms-246338173/246338173 algorithms-210416165721
A short and partial summary of the namesake book by B.Christian and T.Griffiths. I read it for you so you don't have to :)]]>
A short and partial summary of the namesake book by B.Christian and T.Griffiths. I read it for you so you don't have to :)]]> Fri, 16 Apr 2021 16:57:21 GMT /slideshow/algorithms-246338173/246338173 DmitryZinoviev@slideshare.net(DmitryZinoviev) Algorithms DmitryZinoviev A short and partial summary of the namesake book by B.Christian and T.Griffiths. I read it for you so you don't have to :) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/algorithms-210416165721-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A short and partial summary of the namesake book by B.Christian and T.Griffiths. I read it for you so you don&#39;t have to :)
Algorithms from Dmitry Zinoviev
]]> 226 0 https://cdn.slidesharecdn.com/ss_thumbnails/algorithms-210416165721-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Text analysis of The Book Club Play /slideshow/text-analysis-of-the-book-club-play/203753007 presentation-191209211944
An exercise in digital humanities: use Python to extract basic information from a play script.]]>
An exercise in digital humanities: use Python to extract basic information from a play script.]]> Mon, 09 Dec 2019 21:19:44 GMT /slideshow/text-analysis-of-the-book-club-play/203753007 DmitryZinoviev@slideshare.net(DmitryZinoviev) Text analysis of The Book Club Play DmitryZinoviev An exercise in digital humanities: use Python to extract basic information from a play script. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-191209211944-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> An exercise in digital humanities: use Python to extract basic information from a play script.
Text analysis of The Book Club Play from Dmitry Zinoviev
]]> 340 1 https://cdn.slidesharecdn.com/ss_thumbnails/presentation-191209211944-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Exploring the History of Mental Stigma /slideshow/exploring-the-history-of-mental-stigma-155479017/155479017 presentation-190714180358
The majority of the USA population still believe people with serious mental illness (SMI) are dangerous. SMI and dangerousness have been recurring topics of newspaper coverage for some time. What aspects, if any, have been associated in mainstream American media with SMI? Are there historical periods where significant sociopolitical events have occurred e.g., war, economic depression, immigration shifts, that have impacted the balance of positive and negative those aspects?]]>
The majority of the USA population still believe people with serious mental illness (SMI) are dangerous. SMI and dangerousness have been recurring topics of newspaper coverage for some time. What aspects, if any, have been associated in mainstream American media with SMI? Are there historical periods where significant sociopolitical events have occurred e.g., war, economic depression, immigration shifts, that have impacted the balance of positive and negative those aspects?]]> Sun, 14 Jul 2019 18:03:58 GMT /slideshow/exploring-the-history-of-mental-stigma-155479017/155479017 DmitryZinoviev@slideshare.net(DmitryZinoviev) Exploring the History of Mental Stigma DmitryZinoviev The majority of the USA population still believe people with serious mental illness (SMI) are dangerous. SMI and dangerousness have been recurring topics of newspaper coverage for some time. What aspects, if any, have been associated in mainstream American media with SMI? Are there historical periods where significant sociopolitical events have occurred e.g., war, economic depression, immigration shifts, that have impacted the balance of positive and negative those aspects? <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-190714180358-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The majority of the USA population still believe people with serious mental illness (SMI) are dangerous. SMI and dangerousness have been recurring topics of newspaper coverage for some time. What aspects, if any, have been associated in mainstream American media with SMI? Are there historical periods where significant sociopolitical events have occurred e.g., war, economic depression, immigration shifts, that have impacted the balance of positive and negative those aspects?
Exploring the History of Mental Stigma from Dmitry Zinoviev
]]> 455 5 https://cdn.slidesharecdn.com/ss_thumbnails/presentation-190714180358-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Roles and Words in a massive NSSI-Related Interaction Network /slideshow/roles-and-words-in-a-massive-nssirelated-interaction-network/150426706 presentation-190618165008
Non-suicidal self-injury (NSSI), such as self-cutting or self-burning, is the deliberate destruction of ones body tissue in the absence of suicidal intent. Approximately one in five of adolescents and one in four of young adults in the USA often referred to as self-cutters, have engaged in NSSI. The goal of the study is to analyze the topology of an interaction network of the NSSI-related users and compare it to the vocabulary of the blog posts and comments.]]>
Non-suicidal self-injury (NSSI), such as self-cutting or self-burning, is the deliberate destruction of ones body tissue in the absence of suicidal intent. Approximately one in five of adolescents and one in four of young adults in the USA often referred to as self-cutters, have engaged in NSSI. The goal of the study is to analyze the topology of an interaction network of the NSSI-related users and compare it to the vocabulary of the blog posts and comments.]]> Tue, 18 Jun 2019 16:50:07 GMT /slideshow/roles-and-words-in-a-massive-nssirelated-interaction-network/150426706 DmitryZinoviev@slideshare.net(DmitryZinoviev) Roles and Words in a massive NSSI-Related Interaction Network DmitryZinoviev Non-suicidal self-injury (NSSI), such as self-cutting or self-burning, is the deliberate destruction of ones body tissue in the absence of suicidal intent. Approximately one in five of adolescents and one in four of young adults in the USA often referred to as self-cutters, have engaged in NSSI. The goal of the study is to analyze the topology of an interaction network of the NSSI-related users and compare it to the vocabulary of the blog posts and comments. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-190618165008-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Non-suicidal self-injury (NSSI), such as self-cutting or self-burning, is the deliberate destruction of ones body tissue in the absence of suicidal intent. Approximately one in five of adolescents and one in four of young adults in the USA often referred to as self-cutters, have engaged in NSSI. The goal of the study is to analyze the topology of an interaction network of the NSSI-related users and compare it to the vocabulary of the blog posts and comments.
Roles and Words in a massive NSSI-Related Interaction Network from Dmitry Zinoviev
]]> 290 1 https://cdn.slidesharecdn.com/ss_thumbnails/presentation-190618165008-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 A Quaint and Curious Volume of Forgotten Lore, or an Exercise in Digital Humanities /slideshow/presentationa-quaint-and-curious-volume-of-forgotten-lore-or-an-exercise-in-digital-humanities/120376057 presentation-181023030840
Text/network analysis of "Loss of Breath. The Unfinished Life and Death of Edgar Allan Poe," a play in two acts for puppets and masks by Wesley Savick]]>
Text/network analysis of "Loss of Breath. The Unfinished Life and Death of Edgar Allan Poe," a play in two acts for puppets and masks by Wesley Savick]]> Tue, 23 Oct 2018 03:08:40 GMT /slideshow/presentationa-quaint-and-curious-volume-of-forgotten-lore-or-an-exercise-in-digital-humanities/120376057 DmitryZinoviev@slideshare.net(DmitryZinoviev) A Quaint and Curious Volume of Forgotten Lore, or an Exercise in Digital Humanities DmitryZinoviev Text/network analysis of "Loss of Breath. The Unfinished Life and Death of Edgar Allan Poe," a play in two acts for puppets and masks by Wesley Savick <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-181023030840-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Text/network analysis of &quot;Loss of Breath. The Unfinished Life and Death of Edgar Allan Poe,&quot; a play in two acts for puppets and masks by Wesley Savick
A Quaint and Curious Volume of Forgotten Lore, or an Exercise in Digital Humanities from Dmitry Zinoviev
]]> 416 2 https://cdn.slidesharecdn.com/ss_thumbnails/presentation-181023030840-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Network analysis of the 2016 USA presidential campaign tweets /slideshow/network-analysis-of-the-2016-usa-presidential-campaign-tweets/105262525 ic2s2-1018-presentation-180711024417
This is an IC2S2 presentation. Donald Trump has been an avid user of Twitter before, throughout, and in the aftermath of the 2017 USA presidential election campaign. Secretary Hillary Clinton, the Democratic Party candidate, was active on Twitter only from the beginning to end of the campaign. The goal of this research is to reconstruct the timeline and logic of the campaign using complex network analysis of President Trump's and Secretary Clinton's tweets. We analyzed 20,000 tweets posted by Trump in January 2014--December 2017, covering all three major stages of his quest for the Presidency. The tweets are composed of 14,373 distinct, unstemmed terms (words, word combinations, and hashtags). For further analysis, we selected only 300 base terms that occurred in the whole corpus at least 100 times. We grouped the tweets by months into 48 tweet corpora and converted them into a network, based on the similarity of the vocabulary. Each network node represents a monthly corpus. We connected two nodes with a weighted edge if the frequencies of the base terms in the corresponding corpora were strongly correlated (with the Pearson correlation coefficient, serving as the weight of the edge, at or above 0.6). The corpus consists of two clusters: April 2015 (when Clinton announced her intention to run)--March 2016 and April 2016--November 2016 (the general election). Interestingly, the boundary between the clusters corresponds to the campaign stages, too---but those of Trump rather than Clinton: it is located in the middle of the Republican Party primary elections. We hypothesize that throughout the campaign Secretary Clinton was in the defensive position and had to respond to the challenges posed by Trump, rather than form her agenda (at least on Twitter). We will further look into the cross-correlations between the tweet corpora of the two major presidential candidates to get a better sense of their ``leader-follower'' relationship. ]]>
This is an IC2S2 presentation. Donald Trump has been an avid user of Twitter before, throughout, and in the aftermath of the 2017 USA presidential election campaign. Secretary Hillary Clinton, the Democratic Party candidate, was active on Twitter only from the beginning to end of the campaign. The goal of this research is to reconstruct the timeline and logic of the campaign using complex network analysis of President Trump's and Secretary Clinton's tweets. We analyzed 20,000 tweets posted by Trump in January 2014--December 2017, covering all three major stages of his quest for the Presidency. The tweets are composed of 14,373 distinct, unstemmed terms (words, word combinations, and hashtags). For further analysis, we selected only 300 base terms that occurred in the whole corpus at least 100 times. We grouped the tweets by months into 48 tweet corpora and converted them into a network, based on the similarity of the vocabulary. Each network node represents a monthly corpus. We connected two nodes with a weighted edge if the frequencies of the base terms in the corresponding corpora were strongly correlated (with the Pearson correlation coefficient, serving as the weight of the edge, at or above 0.6). The corpus consists of two clusters: April 2015 (when Clinton announced her intention to run)--March 2016 and April 2016--November 2016 (the general election). Interestingly, the boundary between the clusters corresponds to the campaign stages, too---but those of Trump rather than Clinton: it is located in the middle of the Republican Party primary elections. We hypothesize that throughout the campaign Secretary Clinton was in the defensive position and had to respond to the challenges posed by Trump, rather than form her agenda (at least on Twitter). We will further look into the cross-correlations between the tweet corpora of the two major presidential candidates to get a better sense of their ``leader-follower'' relationship. ]]> Wed, 11 Jul 2018 02:44:17 GMT /slideshow/network-analysis-of-the-2016-usa-presidential-campaign-tweets/105262525 DmitryZinoviev@slideshare.net(DmitryZinoviev) Network analysis of the 2016 USA presidential campaign tweets DmitryZinoviev This is an IC2S2 presentation. Donald Trump has been an avid user of Twitter before, throughout, and in the aftermath of the 2017 USA presidential election campaign. Secretary Hillary Clinton, the Democratic Party candidate, was active on Twitter only from the beginning to end of the campaign. The goal of this research is to reconstruct the timeline and logic of the campaign using complex network analysis of President Trump's and Secretary Clinton's tweets. We analyzed 20,000 tweets posted by Trump in January 2014--December 2017, covering all three major stages of his quest for the Presidency. The tweets are composed of 14,373 distinct, unstemmed terms (words, word combinations, and hashtags). For further analysis, we selected only 300 base terms that occurred in the whole corpus at least 100 times. We grouped the tweets by months into 48 tweet corpora and converted them into a network, based on the similarity of the vocabulary. Each network node represents a monthly corpus. We connected two nodes with a weighted edge if the frequencies of the base terms in the corresponding corpora were strongly correlated (with the Pearson correlation coefficient, serving as the weight of the edge, at or above 0.6). The corpus consists of two clusters: April 2015 (when Clinton announced her intention to run)--March 2016 and April 2016--November 2016 (the general election). Interestingly, the boundary between the clusters corresponds to the campaign stages, too---but those of Trump rather than Clinton: it is located in the middle of the Republican Party primary elections. We hypothesize that throughout the campaign Secretary Clinton was in the defensive position and had to respond to the challenges posed by Trump, rather than form her agenda (at least on Twitter). We will further look into the cross-correlations between the tweet corpora of the two major presidential candidates to get a better sense of their ``leader-follower'' relationship. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ic2s2-1018-presentation-180711024417-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This is an IC2S2 presentation. Donald Trump has been an avid user of Twitter before, throughout, and in the aftermath of the 2017 USA presidential election campaign. Secretary Hillary Clinton, the Democratic Party candidate, was active on Twitter only from the beginning to end of the campaign. The goal of this research is to reconstruct the timeline and logic of the campaign using complex network analysis of President Trump&#39;s and Secretary Clinton&#39;s tweets. We analyzed 20,000 tweets posted by Trump in January 2014--December 2017, covering all three major stages of his quest for the Presidency. The tweets are composed of 14,373 distinct, unstemmed terms (words, word combinations, and hashtags). For further analysis, we selected only 300 base terms that occurred in the whole corpus at least 100 times. We grouped the tweets by months into 48 tweet corpora and converted them into a network, based on the similarity of the vocabulary. Each network node represents a monthly corpus. We connected two nodes with a weighted edge if the frequencies of the base terms in the corresponding corpora were strongly correlated (with the Pearson correlation coefficient, serving as the weight of the edge, at or above 0.6). The corpus consists of two clusters: April 2015 (when Clinton announced her intention to run)--March 2016 and April 2016--November 2016 (the general election). Interestingly, the boundary between the clusters corresponds to the campaign stages, too---but those of Trump rather than Clinton: it is located in the middle of the Republican Party primary elections. We hypothesize that throughout the campaign Secretary Clinton was in the defensive position and had to respond to the challenges posed by Trump, rather than form her agenda (at least on Twitter). We will further look into the cross-correlations between the tweet corpora of the two major presidential candidates to get a better sense of their ``leader-follower&#39;&#39; relationship.
Network analysis of the 2016 USA presidential campaign tweets from Dmitry Zinoviev
]]> 245 5 https://cdn.slidesharecdn.com/ss_thumbnails/ic2s2-1018-presentation-180711024417-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Network Analysis of The Shining /slideshow/network-analysis-of-the-shining/86424884 presentation-180119230702
Short slideshow outlining a network analysis of The Shining by Steven King.]]>
Short slideshow outlining a network analysis of The Shining by Steven King.]]> Fri, 19 Jan 2018 23:07:01 GMT /slideshow/network-analysis-of-the-shining/86424884 DmitryZinoviev@slideshare.net(DmitryZinoviev) Network Analysis of The Shining DmitryZinoviev Short slideshow outlining a network analysis of The Shining by Steven King. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-180119230702-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Short slideshow outlining a network analysis of The Shining by Steven King.
Network Analysis of The Shining from Dmitry Zinoviev
]]> 214 1 https://cdn.slidesharecdn.com/ss_thumbnails/presentation-180119230702-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 The Lord of the Ring. A Network Analysis /slideshow/the-lord-of-the-ring-a-network-analysis/80476370 presentation-171005004150
Network analysis of the Lord of the Rings and the Hobbit.]]>
Network analysis of the Lord of the Rings and the Hobbit.]]> Thu, 05 Oct 2017 00:41:50 GMT /slideshow/the-lord-of-the-ring-a-network-analysis/80476370 DmitryZinoviev@slideshare.net(DmitryZinoviev) The Lord of the Ring. A Network Analysis DmitryZinoviev Network analysis of the Lord of the Rings and the Hobbit. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-171005004150-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Network analysis of the Lord of the Rings and the Hobbit.
The Lord of the Ring. A Network Analysis from Dmitry Zinoviev
]]> 854 5 https://cdn.slidesharecdn.com/ss_thumbnails/presentation-171005004150-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Pickling and CSV /slideshow/pickling-and-csv/75220137 picklingandcsv-170420070738
How to use Python for reading/writing CSV files and enjoy persistent storage through pickling?]]>
How to use Python for reading/writing CSV files and enjoy persistent storage through pickling?]]> Thu, 20 Apr 2017 07:07:38 GMT /slideshow/pickling-and-csv/75220137 DmitryZinoviev@slideshare.net(DmitryZinoviev) Pickling and CSV DmitryZinoviev How to use Python for reading/writing CSV files and enjoy persistent storage through pickling? <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/picklingandcsv-170420070738-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> How to use Python for reading/writing CSV files and enjoy persistent storage through pickling?
Pickling and CSV from Dmitry Zinoviev
]]> 5674 5 https://cdn.slidesharecdn.com/ss_thumbnails/picklingandcsv-170420070738-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Python overview /slideshow/python-overview-75219835/75219835 pythonoverview-170420070109
Overview of the most essential core Python functionality for data science.]]>
Overview of the most essential core Python functionality for data science.]]> Thu, 20 Apr 2017 07:01:09 GMT /slideshow/python-overview-75219835/75219835 DmitryZinoviev@slideshare.net(DmitryZinoviev) Python overview DmitryZinoviev Overview of the most essential core Python functionality for data science. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pythonoverview-170420070109-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Overview of the most essential core Python functionality for data science.
Python overview from Dmitry Zinoviev
]]> 209 2 https://cdn.slidesharecdn.com/ss_thumbnails/pythonoverview-170420070109-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Welcome to CS310! /slideshow/welcome-to-cs310/75219715 cs310-01-170420065751
The first presentation from the series accompanying "Data Science Essentials in Python. Collect Organize Explore Predict Value."]]>
The first presentation from the series accompanying "Data Science Essentials in Python. Collect Organize Explore Predict Value."]]> Thu, 20 Apr 2017 06:57:51 GMT /slideshow/welcome-to-cs310/75219715 DmitryZinoviev@slideshare.net(DmitryZinoviev) Welcome to CS310! DmitryZinoviev The first presentation from the series accompanying "Data Science Essentials in Python. Collect Organize Explore Predict Value." <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cs310-01-170420065751-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The first presentation from the series accompanying &quot;Data Science Essentials in Python. Collect Organize Explore Predict Value.&quot;
Welcome to CS310! from Dmitry Zinoviev
]]> 220 5 https://cdn.slidesharecdn.com/ss_thumbnails/cs310-01-170420065751-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Programming languages /slideshow/programming-languages-67045983/67045983 programminglanguages-161012063512
A quick and dirty tour through a variety of programming languages.]]>
A quick and dirty tour through a variety of programming languages.]]> Wed, 12 Oct 2016 06:35:12 GMT /slideshow/programming-languages-67045983/67045983 DmitryZinoviev@slideshare.net(DmitryZinoviev) Programming languages DmitryZinoviev A quick and dirty tour through a variety of programming languages. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/programminglanguages-161012063512-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A quick and dirty tour through a variety of programming languages.
Programming languages from Dmitry Zinoviev
]]> 116 2 https://cdn.slidesharecdn.com/ss_thumbnails/programminglanguages-161012063512-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 The P4 of Networkacy /slideshow/the-p4-of-networkacy/67035087 networkacy-2016-161011234300
Teaching networks and network analysis across the curriculum.]]>
Teaching networks and network analysis across the curriculum.]]> Tue, 11 Oct 2016 23:43:00 GMT /slideshow/the-p4-of-networkacy/67035087 DmitryZinoviev@slideshare.net(DmitryZinoviev) The P4 of Networkacy DmitryZinoviev Teaching networks and network analysis across the curriculum. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/networkacy-2016-161011234300-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Teaching networks and network analysis across the curriculum.
The P4 of Networkacy from Dmitry Zinoviev
]]> 266 5 https://cdn.slidesharecdn.com/ss_thumbnails/networkacy-2016-161011234300-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 DaVinci Code. Network Analysis /slideshow/davinci-code-network-analysis/66197722 presentation-160920054914
An example of how automated text analysis leads, through social network construction, to recognition of key plot lines and characters in a fiction book.]]>
An example of how automated text analysis leads, through social network construction, to recognition of key plot lines and characters in a fiction book.]]> Tue, 20 Sep 2016 05:49:14 GMT /slideshow/davinci-code-network-analysis/66197722 DmitryZinoviev@slideshare.net(DmitryZinoviev) DaVinci Code. Network Analysis DmitryZinoviev An example of how automated text analysis leads, through social network construction, to recognition of key plot lines and characters in a fiction book. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-160920054914-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> An example of how automated text analysis leads, through social network construction, to recognition of key plot lines and characters in a fiction book.
DaVinci Code. Network Analysis from Dmitry Zinoviev
]]> 460 2 https://cdn.slidesharecdn.com/ss_thumbnails/presentation-160920054914-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-DmitryZinoviev-48x48.jpg?cb=1749014206 Dedicated to quality undergraduate teaching and research in the areas of computer networks, social networks, complexity, and network science. Specialties: Social networks, Network Science, Semantic Networks, Software Engineering, Simulation and Modeling. http://networksciencelab.com https://cdn.slidesharecdn.com/ss_thumbnails/codebar-2024-241008024130-72f4a1c4-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/what-you-can-learn-from-obscure-programming-languages/272254040 What You Can Learn fro... https://cdn.slidesharecdn.com/ss_thumbnails/02-basics-240226070604-98eb3fc7-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/machine-learning-basics-for-dummies-no-math/266492664 Machine Learning Basic... https://cdn.slidesharecdn.com/ss_thumbnails/cudan-2023-mukhortova-231215150908-ef40d88d-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/what-is-star-discourse-in-postsoviet-film-journals/264675505 WHat is star discourse...