Issue #205

June 14, 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. GPT best practices [platform.openai.com]
2. Text Editor Data Structures [cdacamar.github.io]
3. Data Compression Drives the Internet. Here’s How It Works. [quantamagazine.org]
4. Artificial brains are helping scientists study the real thing [economist.com]
5. The Little Book [of]
6. Understanding GPT tokenizers [simonwillison.net]
7. Why AI Will Save The World [pmarca.substack.com]

• Health system-scale language models are all-purpose prediction engines (L. Y. Jiang, X. C. Liu, N. P. Nejatian, M. Nasir-Moin, D. Wang, A. Abidin, K. Eaton, H. A. Riina, I. Laufer, et al)
• Faster sorting algorithms discovered using deep reinforcement learning (D. J. Mankowitz, A. Michi, A. Zhernov, M. Gelmi, M. Selvi, C. Paduraru, E. Leurent, S. Iqbal, J.-B. Lespiau, A. Ahern, T. Köppe, K. Millikin, S. Gaffney, et al)
• Estimating the impact of COVID-19 vaccine inequities: a modeling study (N. Gozzi, M. Chinazzi, N. E. Dean, I. M. Longini Jr, M. E. Halloran, N. Perra, A. Vespignani)
• The adoption of non-pharmaceutical interventions and the role of digital infrastructure during the COVID-19 pandemic in Colombia, Ecuador, and El Salvador (N. Gozzi, N. Comini, N. Perra)
• HAT: Hypergraph analysis toolbox (J. Pickard, C. Chen, R. Salman, C. Stansbury, S. Kim, A. Surana, A. Bloch, I. Rajapakse)
• Epidemic spreading in group-structured populations (S. Patwardhan, V. K. Rao, S. Fortunato, F. Radicchi)
• Large Language Models Converge on Brain-Like Word Representations (J. Li, A. Karamolegkou, Y. Kementchedjhieva, M. Abdou, S. Lehmann, A. Søgaard)

Time Series Prediction with LSTMs using TensorFlow 2 and Keras in Python

June 14, 2023

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