Issue #113

July 4, 2021

This weeks Data Science Book is the recently released second edition of " Scientific Computing with Python " by C. Führer, O. Verdier and J. E. Solem which fills an important gap in most Data Scientists bookshelves. High performance computing, specially scientific and numeric computing, is a fairly technical branch of computer science that most programmers aren't really familiar with. It requires a deep understanding of the finer points of the data structures being used and of their specific implementations. With this book the authors have managed to demystify a highly complex subject with clear explanations and analysis that will appeal to developers of all levels. Naturally, some of the subjects could gain from being further developed, but in every project of this kind a balance must be struck between breath and depth. Overall, a useful book that I'll refer to often.

Scientific Computing with Python

1. How Can Data Scientists Use Parallel Processing? [towardsdatascience.com]
2. Random Matrix Theory and Machine Learning [random-matrix-learning.github.io]
3. Why Deep Learning Works Even Though It Shouldn’t [moultano.wordpress.com]
4. Understand Feature Selection in Machine Learning with Python [pub.towardsai.net]
5. Category Theory Illustrated [boris-marinov.github.io]
6. How the Python import system works [tenthousandmeters.com]
7. Binary Trees are optimal… except when they’re not [hbfs.wordpress.com]
8. Solving Machine Learning Performance Anti-Patterns: a Systematic Approach [paulbridger.com]
9. Guide to Reinforcement Learning with Python and TensorFlow [rubikscode.net]
10. Analyzing Financial Data in Python [towardsdatascience.com]

• Is the cure really worse than the disease? The health impacts of lockdowns during COVID-19 (G. Meyerowitz-Katz, S. Bhatt, O. Ratmann, J. M. Brauner, S. Flaxman, S. Mishra, M. Sharma, S. Mindermann, V. Bradley, M. Vollmer, L. Merone, G. Yamey)
• Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (C. Rudin, C. Chen, Z. Chen, H. Huang, L. Semenova, C. Zhong)
• WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset (L. Wang, Y. Li, O. Aslan, O. Vinyals)
• Data vs classifiers, who wins? (L. F. F. Cardoso, V. C. A. Santos, R. S. K. Francês, R. B. C. Prudêncio, R. C. O. Alves)
• On the dynamics of political discussions on Instagram: A network perspective (C. H. G.Ferreira, F. Murai, A. P. C. Silva, J. M. Almeida, M. Trevisan, L. Vassio, M. Mellia, I. Drago)
• How Twitter Interactions Leak Political Trends (M. Solé, F. Giné, M. Valls)
• Clustering of heterogeneous populations of networks (J.-G. Young, A. Kirkley, M. E. J. Newman)

Applying ML on graph-structured data - an introduction to Graph Neural Networks

July 4, 2021

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