Issue #36
February 2, 2020
- 1. DoWhy | Making causal inference easy [Microsoft]
- 2. An Opinionated Guide to ML Research [joschu.net]
- 3. Some Useful Probability Facts for Systems Programming [theartofmachinery.com]
- 4. Climbing the ladder of causality [michielstock.github.io]
- 5. All algorithms implemented in Python (for education) [github.com/TheAlgorithms]
- 6. Leveraging Elastic Demand for Forecasting [tech.instacart.com]
- 7. New perspectives on contextual bandit [microsoft.com]
- 8. An introduction to Bag of Words and how to code it in Python for NLP [FreeCodeCamp]
- • Modeling Echo Chambers and Polarization Dynamics in Social Networks (F. Baumann, P. Lorenz-Spreen, I. M. Sokolov, M. Starnini)
- • The Case for Bayesian Deep Learning (A. G. Wilson)
- • Feature selection in machine learning: Rényi min-entropy vs Shannon entropy (C. Palamidessi, M. Romanelli)
- • Interventions for Ranking in the Presence of Implicit Bias (L. E. Celis, A. Mehrotra, N. K. Vishnoi)
- • stream-learn -- open-source Python library for difficult data stream batch analysis (P. Ksieniewicz, P. Zyblewski)
- • Examining the Benefits of Capsule Neural Networks (A. Punjabi, J. Schmid, A. K. Katsaggelos)
Heatmaps using Matplotlib, Seaborn, and Pandas
All our videos are also available in our YouTube playlist.
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