Issue #208

July 5, 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. Causal Language Models: Bridging the Gap Between Data and Human Understanding [estimand.ai]
2. A PyTorch Approach to ML Infrastructure [run.house]
3. The Rise of the AI Engineer [latent.space]
4. Deep Learning Digs Deep: AI Unveils New Large-Scale Images in Peruvian Desert [blogs.nvidia.com]
5. 9 fintech engineering mistakes [startupwin.kelsus.com]
6. Embracing change and resetting expectations [unlocked.microsoft.com]
7. AI and the automation of work [ben-evans.com]

• How to make your scientific data accessible, discoverable and useful (J. M. Perkel)
• Understanding and combatting misinformation across 16 countries on six continents (A. A. Arechar, J. Allen, A. J. Berinsky, R. Cole, Z. Epstein, K. Garimella, A. Gully, J. G. Lu, R. M. Ross, M. N. Stagnaro, Y. Zhang, G. Pennycook, D. G. Rand)
• Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts (J.D. Zamfirescu-Pereira, R. Y. Wong, B. Hartmann, Q. Yang)
• Circuit Theory for Chemical Reaction Networks (F. Avanzini, N. Freitas, M. Esposito)
• A Survey on Multimodal Large Language Models (S. Yin, C. Fu, S. Zhao, K. Li, X. Sun, T. Xu, E. Chen)
• Everything is Connected: Graph Neural Networks (P. Veličković)
• Generalized contact matrices for epidemic modeling (A. Manna, L. Dall'Amico, M. Tizzoni, M. Karsai, N. Perra)

Interactive Web Visualizations with Bokeh in Python

July 5, 2023

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