Issue #106
June 6, 2021
- 1. Optimize data analytics in capital markets with time-series databases [aws.amazon.com]
- 2. Recommended Data Repositories [nature.com]
- 3. A Concrete Introduction to Probability (using Python) [github.com/norvig]
- 4. Do Wide and Deep Networks Learn the Same Things? [ai.googleblog.com]
- 5. Data Cascades in Machine Learning [ai.googleblog.com]
- 6. Gibbard’s Theorem vs Stable Matching [cdsmithus.medium.com]
- 7. Machine learning is booming in medicine. It’s also facing a credibility crisis [statnews.com]
- 8. How to Ask Useful Questions [joshkaufman.net]
- • Bad machines corrupt good morals (N. Köbis, J.-F. Bonnefon, I. Rahwan)
- • Inference and influence of network structure using snapshot social behavior without network data (A. Godoy-Lorite, N. S. Jones)
- • Synthetic living machines: A new window on life (M. R. Ebrahimkhani. M Levin)
- • Event-Based Backpropagation can compute Exact Gradients for Spiking Neural Networks (T. C. Wunderlich, C. Pehle)
- • Mapping the NFT revolution: market trends, trade networks and visual features (M. Nadini, L. Alessandretti, F. Di Giacinto, M. Martino, L. M. Aiello, A. Baronchelli)
- • Optimizing travel routes using temporal networks constructed from GPS data (T. Mukai, Y. Ikeda)
- • Evaluation of home detection algorithms on mobile phone data using individual-level ground truth (L. Pappalardo, L. Ferres, M. Sacasa, C. Cattuto, L. Bravo)
Functional Python
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