Issue #242

April 25, 2024

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.

Natural Language Processing with Transformers

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

April 25, 2024

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