Issue #244
May 8, 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.
- 1. Understanding Stein's paradox [joe-antognini.github.io]
- 2. Not all Graphs are Trees [buttondown.email]
- 3. LLMs Can’t Do Probability [brainsteam.co.uk]
- 4. Bitcoin Forensic Analysis Uncovers Money Laundering Clusters and Criminal Proceeds [thehackernews.com]
- 5. A Beginner’s Guide to Vector Embeddings [timescale.com]
- 6. SQL Schema Generation With Large Language Models [thenewstack.io]
- 7. What’s Going On in This Graph? [nytimes.com]
- • The Effect of Vaccine Mandates on Disease Spread (R. K. Acton, W. Cao, E. E. Cook, S. A. Imberman, M. F. Lovenheim)
- • Scaling hierarchical agglomerative clustering to trillion-edge graphs (L. Dhulipala, J. Łącki)
- • A Primer on the Inner Workings of Transformer-based Language Models (J. Ferrando, G. Sarti, A. Bisazza, M. R. Costa-jussà)
- • KAN: Kolmogorov-Arnold Networks (Z. Liu, Y. Wang, S. Vaidya, F. Ruehle, J. Halverson, M. Soljačić, T. Y. Hou, M. Tegmark)
- • Network reconstruction via the minimum description length principle (T. P. Peixoto)
- • The Matrix: A Bayesian learning model for LLMs (S. Dalal, V. Misra)
- • The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset (C. Bellei, M. Xu, R. Phillips, T. Robinson, M. Weber, T. Kaler, C. E. Leiserson, Arvind, J. Chen)
Leslie Lamport: Thinking Above the Code
All our videos are also available in our YouTube playlist.
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