Issue #113
July 4, 2021
- 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)
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