Issue #34
January 19, 2020
- 1. An overview of gradient descent optimization algorithms [ruder.io]
- 2. Eleven tips for working with large data sets [nature.com]
- 3. An algorithm that learns through rewards may show how our brain does too [technologyreview.com]
- 4. Overfitting: A Guided Tour [alexpghayes.com]
- 5. Algorithm for Drawing Trees [rachel53461.wordpress.com]
- 6. Understanding Analysis of Variance: ANOVA [towardsdatascience.com]
- 7. Decoding the Black Box: An Important Introduction to Interpretable Machine Learning Models in Python [medium.com/analytics-vidhya]
- 8. Hidden Computational Power Found in the Arms of Neurons [quantamagazine.org]
- 9. Using neural networks to solve advanced mathematics equations [ai.facebook.com]
- 10. Probability Distributions in Data Science [towardsdatascience.com]
- • A modification of the Halpern-Pearl definition of causality (J. Halpern)
- • Challenges of Real-World Reinforcement Learning (G. Dulac-Arnold, D. Mankowitz, T. Hester)
- • Uncovering Coordinated Networks on Social Media (D. Pacheco, P.-M. Hui, C. Torres-Lugo, B. T. Truong, A. Flammini, F. Menczer)
- • Network Information Theoretic Security (H. Zhou, A. E. Gamal)
- • Learning similarity measures from data (B. M. Mathisen, A. Aamodt, K. Bach, H. Langseth)
- • A Complex Networks Approach to Find Latent Clusters of Terrorist Groups (G. M. Campedelli, I. Cruickshank, K. M. Carley)
- • Intelligence, physics and information -- the tradeoff between accuracy and simplicity in machine learning (T. Wu)
The Uncanny Valley of ML
All our videos are also available in our YouTube playlist.
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