Issue #239

April 4, 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. Friends don't let friends export to CSV [kaveland.no]
2. How Quickly Do Large Language Models Learn Unexpected Skills? [quantamagazine.org]
3. A Practitioners Guide to Retrieval Augmented Generation (RAG) [towardsdatascience.com]
4. Kolmogorov Complexity And Compression Distance [smunshi.net]
5. Large language models use a surprisingly simple mechanism to retrieve some stored knowledge [news.mit.edu]
6. Start using ChatGPT instantly [openai.com]
7. How ML Model Data Poisoning Works in 5 Minutes [journal.hexmos.com]

• The language of a virus (Y.-A. Kim, T. M. Przytycka)
• Dissimilarity between synchronization processes on networks (A. P. Riascos)
• Raphtory: The temporal graph engine for Rust and Python (B. Steer, N. A. Arnold, C. Tidiane, R. Lambiotte, H. Yousaf, L. Jeub, F. Murariu, S. Kapoor, P. Rico, R. Chan, L. Chan, J. Alford, R. G. Clegg, F. Cuadrado, M. R. Barnes, P. Zhong, J. Pougué-Biyong, A. Alnaimi)
• Rationalism in the face of GPT hypes: Benchmarking the output of large language models against human expert-curated biomedical knowledge graphs (N. S. Babaiha, S. G. Rao, J. Klein, B. Schultz, M. Jacobs, M. Hofmann-Apitius)
• Large Language Models: A Survey (S. Minaee, T. Mikolov, N. Nikzad, M. Chenaghlu, R. Socher, X. Amatriain, J. Gao)
• Generative Agents: Interactive Simulacra of Human Behavior (J. S. Park, J. C. O'Brien, C. J. Cai, M. R. Morris, P. Liang, M. S. Bernstein)
• A Review of Graph Neural Networks in Epidemic Modeling (Z. Liu, G. Wan, B. A. Prakash, M. S. Y. Lau, W. Jin)

A little guide to building Large Language Models in 2024

April 4, 2024

​