Source: Deep Learning on Medium
D4S Sunday Briefing #29
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Dec 15, 2019
Thank you for joining us for this weeks issue of the Sunday Briefing. This week we focus on the big picture with Stanford’s AI Index Report and Joshua Bengio’s push for causality in AI. We get in the weeds with a Microsoft blog post on how to search for efficient neural architectures, a review of ML in the physical Sciences, an introduction to Recurrent Neural Networks an overview of logistic regression models in aggregated data.
Finally, in the video of the week Patrick Winston give us a thorough overview of Support Vector Machines.
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The D4S team
Tutorials and blog posts that came across our desk this week.
- Artificial Intelligence Index Report 2019 [hai.stanford.edu]
- Quantum computing gains a first foothold in investment banking [medium.com/@IBMResearch]
- An AI Pioneer Wants His Algorithms to Understand the ‘Why’ [wired.com]
- When did societies become modern? ‘Big history’ dashes popular idea of Axial Age [nature.com]
- Project Petridish: Efficient forward neural architecture search [microsoft.com]
- The Huge, Unseen Operation Behind the Accuracy of Google Maps [wired.com]
- Building a search engine from scratch [0x65.dev]
Fresh off the press:
Some of the most interesting academic papers published recently.
- Machine learning and the physical sciences (G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto, L. Zdeborová)
- Detecting and quantifying causal associations in large nonlinear time series datasets (J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic)
- Markov chain approach to model intertemporal choices and coverages in air transport markets (D. O. Cajueiro, F. N. Mundim, J. I. F. Martins, P. A. M. Sakowski, R. F. S. Andrade)
- Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (A. Olteanu, C. Castillo, F. Diaz, E. Kıcıman)
- Recurrent Neural Networks (RNNs): A gentle Introduction and Overview (R. M. Schmidt)
- Animal daily mobility patterns analysis using resting event networks (M. Lenormand, H. Pella, H. Capra)
- The mathematical structure of innovation (T. M. A. Fink, A. Teimouri)
- Logistic regression models for aggregated data (T. Whitaker, B. Beranger, S. A. Sisson)
Video of the week:
Interesting discussions, ideas or tutorials that came across our desk.
Support Vector Machines
Opportunities to learn from us
- Jan 17, 2019 — Time Series for Everyone [Register]
- Jan 27, 2019 — Applied Probability Theory for Everyone [Register] 🆕
- Mar 15–16, 2019 — Time series modeling: ML and deep learning approaches — Strata/AI [Register]
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