Issue #46
April 12, 2020
- 1. How We Test Vector [vector.dev]
- 2. Filling in PDF forms with Python [yoongkang.com]
- 3. Untangling Microservices, or Balancing Complexity in Distributed Systems [vladikk.com]
- 4. Why pie charts often suck [medium.com/the-mission]
- 5. On Mathematical Notation and Communication in Machine Learning [medium.com/dataseries]
- 6. The Approximation Power of Neural Networks [towardsdatascience.com]
- • The unreasonable effectiveness of deep learning in artificial intelligence (T. J. Sejnowski)
- • Crowding reveals fundamental differences in local vs. global processing in humans and machines (A. Doerig, A. Bornet, O. H. Choung, M. H. Herzog)
- • Toward an Integration of Deep Learning and Neuroscience (A. H. Marblestone, G. Wayne, K. P. Kording)
- • On Anomaly Interpretation via Shapley Values (N. Takeishi, Y. Kawahara)
- • Trust in Recommender Systems: A Deep Learning Perspective (M. Dong, F. Yuan, L. Yao, X. Wang, X. Xu, L. Zhu)
- • The Twitter Social Mobility Index: Measuring Social Distancing Practices from Geolocated Tweets (P. Xu, M. Dredze, D. A. Broniatowski)
- • Inside the Mind of a Stock Market Crash (S. Giglio, M. Maggiori, J. Stroebel, S. Utkus)
- • A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models (D. Liu, L. Clemente, C. Poirier, X. Ding, M. Chinazzi, J. T. Davis, A. Vespignani, M. Santillana)
Rob Chew, Peter Baumgartner | Connected: A Social Network Analysis Tutorial with NetworkX
All our videos are also available in our YouTube playlist.
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