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Get the answers and support you need.
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Stay updated with the latest podcasting tips and trends.
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Create the best live podcast and engage your audience.
Tips on making the decision to monetize your podcast.
The best ways to get more eyes and ears on your podcast.
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The ultimate guide to recording a podcast on your phone.
Steps to set up and use group recording in the Podbean app.
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 59: How to fool a smart camera with deep learning
Episode 58: There is physics in deep learning!
Episode 57: Neural networks with infinite layers
Episode 56: The graph network
Episode 55: Beyond deep learning
Episode 54: Reproducible machine learning
Episode 53: Estimating uncertainty with neural networks
Episode 52: why do machine learning models fail? [RB]
Episode 51: Decentralized machine learning in the data marketplace (part 2)
Episode 50: Decentralized machine learning in the data marketplace
Episode 49: The promises of Artificial Intelligence
Episode 48: Coffee, Machine Learning and Blockchain
Episode 47: Are you ready for AI winter? [Rebroadcast]
Episode 46: why do machine learning models fail? (Part 2)
Episode 45: why do machine learning models fail?
Episode 44: The predictive power of metadata
Episode 43: Applied Text Analysis with Python (interview with Rebecca Bilbro)
Episode 42: Attacking deep learning models (rebroadcast)
Episode 41: How can deep neural networks reason
Episode 40: Deep learning and image compression
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