Issue #92
February 28, 2021
The field of Network Science has developed quickly since its birth in the late 90s with the introduction of many different concepts and techniques originating from different fields. "A First Course in Network Science" by F. Menczer, S. Fortunato and C. A. Davis, guides you through the theoretical and practical framework needed to understand the basic principles of network science and how you might apply it in your own work. The book is complemented by a GitHub repository packed full of Python examples to help you better understand the concepts as they are introduced.
- 1. Cross-database queries in SQLite [simonwillison.net]
- 2. Complexity no Bar to AI [gwern.net]
- 3. Machine Learning for Computer Architecture [ai.googleblog.com]
- 4. Mathematicians Set Numbers in Motion to Unlock Their Secrets [quantamagazine.org]
- 5. Scientists begin building highly accurate digital twin of our planet [ethz.ch]
- 6. How to Efficiently Choose the Right Database for Your Applications [pingcap.com]
- 7. A Vim Guide for Advanced Users [thevaluable.dev]
- 8. Practical Color Theory [tallys.github.io]
- 9. The Easiest Unsolved Problem in Graph Theory [medium.com/cantors-paradise]
- • Knowledge Graphs (C. Gutierrez, J. F. Sequeda)
- • Evaluating epidemic forecasts in an interval format (J. Bracher, E. L. Ray, T. Gneiting, N. G. Reich)
- • Learning Curve Theory (M. Hutter)
- • Topological Graph Neural Networks (M. Horn, E. De Brouwer, M. Moor, Y. Moreau, B. Rieck, K. Borgwardt)
- • The mobility laws of location-based games (L. Tonetto, E. Lagerspetz, A. Y. Ding, J. Ott, S. Tarkoma, P. Nurmi)
- • The Value of Big Data for Credit Scoring: Enhancing Financial Inclusion using Mobile Phone Data and Social Network Analytics (M. Óskarsdóttir, C. Bravo, C. Sarraute, J. Vanthienen, B. Baesens)
Functional Programming
All our videos are also available in our YouTube playlist.
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