Issue #118
July 4, 2021
Dear friends,
Welcome to the 118th issue of the Sunday Briefing.
This week we have a new post in the V4Sci substack: Candlestick Chart: Visualizing Stock prices with Matploltib where we explore a centenarian visualization for pricing information. You can also catch up on : How to model the effects of vaccination, the latest post on the Epidemiology series that is now available in medium and you can find the respective code in the Epidemiology GitHub repo. While over at G4Sci you can now read The Watts-Strogatz Model and the Small World Effect, the second post of the series were we explore simple theoretical models of networks.
Tomorrow we have our webinar on Causal Inference: Why and What If – Causal Analysis for Everyone. There’s still a few spots left so Register now and make sure you don’t miss it!
This week we have an overview of what language models have learned, B-Trees, Quantile Regression and on When Correlation is Better than Causation.
From the halls of academia we consider Text Data Augmentation for Deep Learning, the Dynamics of majority rule on hypergraphs and Program Synthesis with Large Language Models.
Finally, this weeks ‘Data Science Book ’ highlight is the “Feynman’s Lectures on Computation” by R. P. Feynman. As always you can find all the previous book recommendations on our website. In the video of the week we have a vide on the 7 Reasons Most Machine Learning Funds Fail.
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Semper discentes,
The D4S Team
This weeks Data Science Book is the most excellent "Feynman's Lectures on Computation" by R. P. Feynman. You might be familiar with Feynman's Lectures on Physics, but his lectures on Computation (based on a class he taught and his work in 'Connection Machine') aren't any less amazing. Through this short book, Feynman guides us through the concept of computation and the van Neumann architecture in his unique style, from logic functions, to Turing machines, coding and even quantum computers. While not directly related to Data Science, it will give you a unique appreciation of the finer points in which computers are "Dumb as hell but go like mad" so that you can better squeeze every bit of performance out of your code.
- 1. What Have Language Models Learned? [pair.withgoogle.com]
- 2. B-Trees: More Than I Thought I'd Want to Know [benjamincongdon.me]
- 3. Quantile Regression [hackmd.io]
- 4. What is category theory and why is it trendy? [katyhristova.medium.com]
- 5. When Correlation is Better than Causation [narrator.ai]
- 6. Build your own Grammarly in Python [towardsdatascience.com]
- 7. The Square Root Algorithm [cantorsparadise.com]
- • Interplay between population density and mobility in determining the spread of epidemics in cities (S. Hazarie, D. Soriano-Paños, A. Arenas, J. Gómez-Gardeñes, G. Ghoshal)
- • Text Data Augmentation for Deep Learning (C. Shorten, T. M. Khoshgoftaar, B. Furht)
- • An Elementary Introduction to Information Geometry (F. Nielsen)
- • Dynamics of majority rule on hypergraphs (J. Noonan, R. Lambiotte)
- • Self-testing and vaccination against COVID-19 to minimize school closure (E. Colosi, G. Bassignana, D. A. Contreras, C. Poirier, S. Cauchemez, Y. Yazdanpanah, B. Lina, A. Fontanet, A. Barrat, V. Colizza)
- • Program Synthesis with Large Language Models (J. Austin, A. Odena, M. Nye, M. Bosma, H. Michalewski, D. Dohan, E. Jiang, C. Cai, M. Terry, Q. Le, C. Sutton)
- • Knowledge Graphs 2021: A Data Odyssey (G. Weikum)
The 7 Reasons Most Machine Learning Funds Fail
All our videos are also available in our
YouTube playlist.
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