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Delivering unstoppable data with Streamr (Ep. 154)

Delivering unstoppable data to unstoppable apps is now possible with Streamr Network
Streamr is a layer zero protocol for real-time data which powers the decentralized Streamr pub/sub network. The technology works in tandem with companion blockchains - currently Ethereum and xDai chain - which are used for identity, security and payments. On top is the application layer, including the Data Union framework, Marketplace and Core, and all third party applications. 
In this episode I have a very interesting conversation with Streamr founder and CEO Henri Pihkala
 
References
Streamr project website: https://streamr.network/ 
More about the Streamr Network: https://streamr.network/discover/network 
More about Data Unions: https://streamr.network/discover/data-unions
More about the Data Marketplace: https://streamr.network/discover/marketplace 
Developer docs: https://streamr.network/docs 
Streamr Github: https://github.com/streamr-dev 
Streamr Discord: https://discord.gg/gZAm8P7hK8 
Streamr Twitter: https://twitter.com/streamr 
Streamr YouTube: https://www.youtube.com/channel/UCGWEA61RueG-9DV53s-ZyJQ 
Streamr Reddit: https://reddit.com/r/streamr 
Scalability & latency research blog: https://blog.streamr.network/streamr-network-performance-and-scalability-whitepaper/ 
Swash, a Data Union built on Streamr: https://swashapp.io/ 
 

Can I get paid for my data? With Mike Andi from Mytiki (Ep. 150)

Your data is worth thousands a year. Why aren’t you getting your fair share? There is a company that has a mission: they want you to take back control and get paid for your data.
In this episode I speak about knowledge graphs, data confidentiality and privacy with Mike Audi, CEO of MyTiki.
 
 
You can reach them on their website https://mytiki.com/
 
Discord official channel
https://discord.com/invite/evjYQq48Be
 
Telegram
https://t.me/mytikiapp
 
Signal
https://signal.group/#CjQKIA66Eq2VHecpcCd-cu-dziozMRSH3EuQdcZJNyMOYNi5EhC0coWtjWzKQ1dDKEjMqhkP
 
 

[RB] Complex video analysis made easy with Videoflow (Ep. 75)

In this episode I am with Jadiel de Armas, senior software engineer at Disney and author of Videflow, a Python framework that facilitates the quick development of complex video analysis applications and other series-processing based applications in a multiprocessing environment. 
I have inspected the videoflow repo on Github and some of the capabilities of this framework and I must say that it’s really interesting. Jadiel is going to tell us a lot more than what you can read from Github 
 
References
Videflow Github official repository https://github.com/videoflow/videoflow
 

How to cluster tabular data with Markov Clustering (Ep. 73)

In this episode I explain how a community detection algorithm known as Markov clustering can be constructed by combining simple concepts like random walks, graphs, similarity matrix. Moreover, I highlight how one can build a similarity graph and then run a community detection algorithm on such graph to find clusters in tabular data.
You can find a simple hands-on code snippet to play with on the Amethix Blog 
Enjoy the show! 
 
References
[1] S. Fortunato, “Community detection in graphs”, Physics Reports, volume 486, issues 3-5, pages 75-174, February 2010.
[2] Z. Yang, et al., “A Comparative Analysis of Community Detection Algorithms on Artificial Networks”, Scientific Reports volume 6, Article number: 30750 (2016)
[3] S. Dongen, “A cluster algorithm for graphs”, Technical Report, CWI (Centre for Mathematics and Computer Science) Amsterdam, The Netherlands, 2000.
[4] A. J. Enright, et al., “An efficient algorithm for large-scale detection of protein families”, Nucleic Acids Research, volume 30, issue 7, pages 1575-1584, 2002.

Episode 67: Classic Computer Science Problems in Python

Today I am with David Kopec, author of Classic Computer Science Problems in Python, published by Manning Publications.
His book deepens your knowledge of problem solving techniques from the realm of computer science by challenging you with interesting and realistic scenarios, exercises, and of course algorithms. There are examples in the major topics any data scientist should be familiar with, for example search, clustering, graphs, and much more.
Get the book from https://www.manning.com/books/classic-computer-science-problems-in-python and use coupon code poddatascienceathome19 to get 40% discount.
 
References
Twitter https://twitter.com/davekopec
GitHub https://github.com/davecom
classicproblems.com

Episode 56: The graph network

Since the beginning of AI in the 1950s and until the 1980s, symbolic AI approaches have dominated the field. These approaches, also known as expert systems, used mathematical symbols to represent objects and the relationship between them, in order to depict the extensive knowledge bases built by humans. The opposite of the symbolic AI paradigm is named connectionism, which is behind the machine learning approaches of today