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🥂🍾 D4S Sunday Briefing #161 🍾🥂
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Jun 26, 2022
Welcome to the June 26th edition of the Sunday Briefing where we continue our 3rd anniversary celebration with another small change to our design. Starting this week, we’re highlighting the next upcoming webinar right on top of each edition.
This week we’re on hiatus from blogging, but you can still catch up on our recent posts. First, we celebrate World Goth Day with the latest V4Sci post “Unknown Pleasures Plot: Recreating an iconic album cover and your favorite 80s t-shirt”. Over at Medium, we have “Exploring the Dow-Jones Industrial Average using Linear Regression”. You can also catch up on the latest post in the G4Sci series: Prim’s Minimum Spanning Tree Algorithm: Finding the shortest path to every node.
In addition to the three new webinars we announced in these months issues (Deep Learning for Everyone on July 7the, Time Series for Everyone on July 26th and Advanced Time Series for Everyone on Aug 9) we’re proud to announce the latest edition of Natural Language Processing (NLP) for Everyone on Aug 25th!
On our regularly scheduled content we explore where all the crypto use cases are, why LaMDA’s Sentience is Nonsense, Why Life Always Seems to Get More Complicated and how the Internet Origin Story You Know Is Wrong.
While on the more academic front we learn about the Bayesian Workflow, the Eigenvalue ratio statistics of complex networks, a Bounded-Confidence Model of Opinion Dynamics with Heterogeneous Node-Activity Levels and Link Prediction on Complex Networks.
This weeks ‘Data Science Book’ highlight is Data Science Book is “Analytical Skills for AI & Data Science” by D. Vaughan. As always you can find all the previous book recommendations on our website. In the video of the week we have an MIT lecture on Bayesian Statistics.
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The D4S Team
The latest post on the Visualization for Data Science substack: Unknown Pleasures Plot: Recreating an iconic album cover and your favorite 80s t-shirt is now out, Don’t forget to Subscribe so you’re first in line to receive every post.
The latest post on the Graphs for Data Science substack: Prim’s Minimum Spanning Tree Algorithm: Finding the shortest path to every node is now out.You should Sign Up to make sure you never miss a post!
The latest post in the CoVID-19 series, ‘How to model the effects of vaccination’ takes a look at how simple modifications of the SIR model can help us better understand how vaccines work. As usual, all the code is available in GitHub: http://github.com/DataForScience/Epidemiology101
The latest post in the Causality series covers section ‘3.7 — Mediation’, a recipe to calculate the controlled directed effect. The code for each blog post in this series is hosted by a dedicated GitHub repository: https://github.com/DataForScience/Causality
This weeks Data Science Book is “Analytical Skills for AI & Data Science” by D. Vaughan. This is an unusual book that takes a holistic approach to AI and Data Science from a Business perspective. Aimed at managers with limited Data Science experience, this book uses increasingly complex practical examples to introduce a wide range of concepts and analytical techniques. Even if you have no direct interest in AI and Data Science, this book will give you enough background knowledge to be able to successfully manage Data Scientists, break down complex problems into individual components and help guide your team towards the right solution to your business problems.
Tutorials and blog posts that came across our desk this week.
- LaMDA’s Sentience is Nonsense — Here’s Why [lastweekin.ai]
- Entropy: Why Life Always Seems to Get More Complicated [https://jamesclear.com]
- Researchers Can Duplicate Keys from the Sounds They Make in Locks [kottke.org]
- YaLM 100B parameter language model [github.com/yandex]
- Introducing PyScript [lwn.net]
- Dolt is Git for Data! [github.com/dolthub]
- The Internet Origin Story You Know Is Wrong [wired.com]
- A Data-Centric Introduction to Computing [dcic-world.org]
- Where are all the crypto use cases? [evanjconrad.com]
- Does one simple rule underlie learning in the brain? [actu.epfl.ch]
Some of the most interesting academic papers published recently
- Eigenvalue ratio statistics of complex networks: Disorder versus randomness (A. Mishra, T. Raghav, S. Jalan)
- Bayesian Workflow (A. Gelman, A. Vehtari, D. Simpson, C. C. Margossian, B. Carpenter, Y. Yao, L. Kennedy, J. Gabry, P.-C. Bürkner, M. Modrák)
- A Hybrid Adjacency and Time-Based Data Structure for Analysis of Temporal Networks (T. Hilsabeck, M. Arastuie, K. S. Xu)
- Diffusion approximation of a network model of meme popularity (K. A. Oliveira, S. Unicomb, J. P. Gleeson)
- A Bounded-Confidence Model of Opinion Dynamics with Heterogeneous Node-Activity Levels (G. J. Li, M. A. Porter)
- LinkBERT: Language Model Pretraining with Document Link Knowledge (M. Yasunaga, J. Leskovec, P. Liang)
- Intervention strategies for epidemic spreading on bipartite metapopulation networks (B. Wang, L. Yang, Y. Han)
- Link Prediction on Complex Networks: An Experimental Survey (H. Wu, C. Song, Y. Ge, T. Ge)
Interesting discussions, ideas or tutorials that came across our desk
All the videos of the week are now available in our Youtube playlist.
Opportunities to learn from us:
- Jul 07, 2022 — Deep Learning for Everyone [Register] 🆕
- Jul 26, 2022 — Time Series for Everyone [Register] 🆕
- Aug 9, 2022 — Advanced Time Series for Everyone [Register] 🆕
- Aug 25, 2022 — Natural Language Processing for Everyone [Register] 🆕
Long form tutorials:
- Natural Language Processing 5.5h, covering basic and advancing techniques using NLTK and Keras
- Times Series Analysis for Everyone 6h covering data pre-processing, visualization, ARIMA, ARCH and Deep Learning models
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