Issue #101
May 2, 2021
This weeks Data Science Book is " Data Analysis: A Bayesian Tutorial " by D. S. Sivia and J. Skilling. Bayesian analysis is a statistical approach with a long and rich history that allows us to use probability statements to quantify our uncertainty about specific parameters. This short book provides an excellent first introduction to this powerful family of techniques with practical examples. The book quickly guides us from the fundamental intuition behind Bayes theorem more advanced concepts and applications such as Model comparison, Inference and Non-Parametric Estimation.
- 1. The Turing Test is obsolete. It’s time to build a new barometer for AI [fastcompany.com]
- 2. The Agency Trilemma and ACM [cacm.acm.org]
- 3. The Fourier transform is a neural network [sidsite.com]
- 4. What AI Can Teach Us About the Myth of Human Genius [theatlantic.com]
- 5. Evolution of random number generators [johndcook.com]
- 6. A Learning Theoretic Perspective on Local Explainability [blog.ml.cmu.edu]
- 7. Hopfield Networks is All You Need [ml-jku.github.io]
- • Belief propagation for networks with loops (A. Kirkley, G. T. Cantwell, M. E. J. Newman)
- • The Times They Are Rarely A-Changin' - Circadian Regularities in Social Media Use (S. Kates, J. Tucker, J. Nagler, R. Bonneau)
- • Text Classification Algorithms: A Survey (K. Kowsari, K. J. Meimandi, M. Heidarysafa, S. Mendu, L. Barnes, D. Brown)
- • A Review of Formal Methods applied to Machine Learning (C. Urban, A. Miné)
- • The Production and Consumption of Social Media (A. Filippas, J. Horton)
- • Real-time Data Infrastructure at Uber (Y. Fu, C. Soman)
- • Why AI is Harder Than We Think (M. Mitchell)
Selenium Tutorial For Beginners
All our videos are also available in our YouTube playlist.
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