Issue #103

May 16, 2021

This weeks Data Science Book is " Data Analysis: A Bayesian Tutorial " by D. S. Sivia and J. Skilling. Bayesian analysis is a statistical approach with a long and rich history that allows us to use probability statements to quantify our uncertainty about specific parameters. This short book provides an excellent first introduction to this powerful family of techniques with practical examples. The book quickly guides us from the fundamental intuition behind Bayes theorem more advanced concepts and applications such as Model comparison, Inference and Non-Parametric Estimation.

Data Analysis: A Bayesian Tutorial

1. The Brain Maps Out Ideas and Memories Like Spaces [quantamagazine.org]
2. Microsoft is shutting down its Azure Blockchain Service [zdnet.com]
3. Building a new vector based storage model [questdb.slab.com]
4. Modern Monetary Theory, explained [vox.com]
5. Recursion in Python: An Introduction [realpython.com]
6. Clustergam: visualisation of cluster analysis [martinfleischmann.net]
7. How image search works at Dropbox [dropbox.tech]
8. Datasets on arXiv [medium.com/paperswithcode]
9. Top Ten Git Tips & Tricks [honeybadger.io]
10. Extracting Data from Tracking Devices [jeffhuang.com]

• A global database of COVID-19 vaccinations (E. Mathieu, H. Ritchie, E. Ortiz-Ospina, M. Roser, J. Hasell, C. Appel, C. Giattino, L. Rodés-Guirao)
• Prediction of new scientific collaborations through multiplex networks (M. Tuninetti, A. Aleta, D. Paolotti, Y. Moreno, M. Starnini)
• Consistency landscape of network communities (D. Lee, S. H. Lee, B. J. Kim, H. Kim)
• Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges (M. M. Bronstein, J. Bruna, T. Cohen, P. Veličković)
• The distance backbone of complex networks (T. Simas, R. B. Correia, L. M. Rocha)
• Improving Sequence Modeling Ability of Recurrent Neural Networks via Sememes (Y. Qin, F. Qi, S. Ouyang, Z. Liu, C. Yang, Y. Wang, Q. Liu, M. Sun)
• Flow-based Community Detection in Hypergraphs (A. Eriksson, T. Carletti, R. Lambiotte, A. Rojas, M. Rosvall)
• A Survey of Data Augmentation Approaches for NLP (S. Y. Feng, V. Gangal, J. Wei, S. Chandar, S. Vosoughi, T. Mitamura, E. Hovy)

Geometric Deep Learning Past, Present, And Future

May 16, 2021

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