Issue #207

June 28, 2023

This week’s Data Science Book, "Network Science with Python", by D. Knickerbocker, is a highly recommended book for anyone interested in network analysis. It provides a comprehensive and accessible introduction to the topic. The book's linear progression and friendly tone make it highly engaging and easy to follow. The author's contagious enthusiasm and practical examples effectively communicate the power and importance of network analysis. The book covers various domains, including language and social media data mining, and explores the relationship between NLP and networks, an approach similar to our very own Graphs for Data Science substack. It emphasizes the value of actionable insights in the conversational AI domain and provides historical context and real-world use cases for NLP solutions. The book also introduces the Python packages used and dives into network science using the NetworkX library. It demonstrates how graphs can be used in machine learning and covers important concepts like betweenness centrality, page rank, and community detection with real-world applications. Overall, "Network Science with Python" is a well-written and comprehensive guide that offers practical insights and is suitable for readers of all levels.

Network Science with Python

1. NVIDIA H100 GPUs Set Standard for Generative AI in Debut MLPerf Benchmark [blogs.nvidia.com]
2. What are embeddings? [vickiboykis.com]
3. What Is a Transformer Model? [blogs.nvidia.com]
4. LLM Powered Autonomous Agents [lilianweng.github.io]
5. The Magic of Embeddings [stack.convex.dev]
6. Patterns of Distributed Systems [martinfowler.com]
7. Integrated information theory [scholarpedia.org]

• Monthly excess mortality across counties in the United States during the COVID-19 pandemic, March 2020 to February 2022 (E. Paglino, D. J. Lundberg, Z. Zhou, J. A. Wasserman, R. Raquib, A. N. Luck, K. Hempstead, J. Bor, S. H. Preston, I. T. Elo, A. C. Stokes)
• Emergence of Geometric Turing Patterns in Complex Networks (J. van der Kolk, G. García-Pérez, N. E. Kouvaris, M. Á. Serrano, M. Boguñá)
• Social Media? Get Serious! Understanding the Functional Building Blocks of Social Media (J. Kietzmann, K. Hermkens, I. P. McCarthy, B. Silvestre)
• Unifying Large Language Models and Knowledge Graphs: A Roadmap (S. Pan, L. Luo, Y. Wang, C. Chen, J. Wang, X. Wu)
• From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought (L. Wong, G. Grand, A. K. Lew, N. D. Goodman, V. K. Mansinghka, J. Andreas, J. B. Tenenbaum)
• Textbooks Are All You Need (S. Gunasekar, Y. Zhang, J. Aneja, C. C. T. Mendes, A. Del Giorno, S. Gopi, M. Javaheripi, P. Kauffmann, G. de Rosa, O. Saarikivi, A. Salim, S. Shah, H. S. Behl, X. Wang, S. Bubeck, R. Eldan, A. T. Kalai, Y. T. Lee, Y. Li)
• Any Deep ReLU Network is Shallow (M. J. Villani, N. Schoots)

OpenAI Embeddings API - Searching Financial Documents

June 28, 2023

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