Issue #242

Apr 25, 2024

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Dear Reader,

Welcome to the April 25th edition of the Data Science Briefing!

The latest medium post on the Epidemiology series: Epidemiology 303: Metapopulation models, where we explore how to connect multiple populations through a travel matrix is now out. You can catch up on the latest in the Graphs for Data Science substack: k-core Decomposition, or in the Viz4Sci series, The Effects of Vaccination.

This week, we're proud to announce the latest webinar series on Generative AI: LangChain for Generative AI Pipelines has just been announced for June 5th, and the Registrations are already open. Don't miss out!

In our regularly scheduled content, we explore Financial Market Applications of LLMs, The Math Behind “The Curse of Dimensionality,” and a hacker's guide to Language Models.

On the academic front, we learn about Causal machine learning for predicting treatment outcomes, explore superblockify: A Python Package for Automated Generation, Visualization, and Analysis of Potential Superblocks in Cities and An impossibility result for Markov Chain Monte Carlo sampling from micro-canonical bipartite graph ensembles.

This week's book recommendation is "Natural Language Processing with Transformers" by L. Tunstall, L. von Werra, and T. Wolf. You can find all the previous book recommendations on our website. In this week's video, we have Making AI accessible with Andrej Karpathy and Stephanie Zhan.

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Semper discentes,

The D4S Team

This week's book is "Natural Language Processing with Transformers" by L. Tunstall, L von Werra and T. Wolf. As an avid natural language processing enthusiast (NLP), I recently delved into "Natural Language Processing with Transformers" with great anticipation. Authored by experts in the field, this book not only met but exceeded my expectations, offering a comprehensive exploration of the groundbreaking advancements in NLP powered by transformers.

From the outset, the book strikes an excellent balance between theoretical underpinnings and practical applications. Including code snippets and implementation tips further enhances the learning experience, allowing readers to gain proficiency in applying these powerful techniques to real-world problems.

In conclusion, "Natural Language Processing with Transformers" is a must-read for anyone interested in unlocking the full potential of modern NLP techniques. Whether you're a researcher, a student, or a practitioner seeking to stay ahead of the curve, this book offers a treasure trove of knowledge and practical wisdom. Engaging, informative, and inspiring, it is sure to leave a lasting impact on anyone passionate about the intersection of language and technology.

1. ​Financial Market Applications of LLMs [thegradient.pub]
2. ​The Math Behind “The Curse of Dimensionality”​ [towardsdatascience.com]
3. ​That’s Not Physics [aps.org]
4. ​The Illustrated Word2vec​ [jalammar.github.io]
5. ​A hacker's guide to Language Models​ [github.com/fastai]
6. ​Understanding What Matters for LLM Ingestion and Preprocessing [unstructured.io]
7. ​Entity Resolved Knowledge Graphs [senzing.com]
8. ​Anthropic Prompt library​ [docs.anthropic.com]

• ​Changes in social norms during the early stages of the COVID-19 pandemic across 43 countries​ (G. Andrighetto, A. Szekely, A. Guido, M. Gelfand, J. Abernathy, G. Arikan, Z. Aycan, S. Bankar, D. Barrera, D. Basnight-Brown, A. Belaus, E. Berezina, et al.)
• ​YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories​ (T. Yabe, K. Tsubouchi, T. Shimizu, Y. Sekimoto, K. Sezaki, E. Moro, A. Pentland)
• ​Causal machine learning for predicting treatment outcomes​ (S. Feuerriegel, D. Frauen, V. Melnychuk, J. Schweisthal, K. Hess, A. Curth, S. Bauer, N. Kilbertus, I. S. Kohane, M. van der Schaar)
• ​Many-Shot In-Context Learning​ (R. Agarwal, A. Singh, L. M. Zhang, B. Bohnet, S. Chan, A. Anand, Z. Abbas, A. Nova, J. D. Co-Reyes, E. Chu, F. Behbahani, A. Faust, H. Larochelle)
• ​Deep Neural Networks via Complex Network Theory: a Perspective​ (E. La Malfa, G. La Malfa, G. Nicosia, V. Latora)
• ​superblockify: A Python Package for Automated Generation, Visualization, and Analysis of Potential Superblocks in Cities​ (C. M. Büth, A. Vybornova, M. Szell)
• ​An impossibility result for Markov Chain Monte Carlo sampling from micro-canonical bipartite graph ensembles​ (G. Preti, G. D. F. Morales, M. Riondato)

Making AI accessible with Andrej Karpathy and Stephanie Zhan

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All the videos of the week are now available in our Youtube playlist.