Welcome to the 257th edition of the Data Science Briefing!
The next edition of the LangChain for Generative AI Pipelines webinar is on September 25th. There are only a few spots left, so Register now!
The most recent post on the Epidemiology series, Epidemiology 303: Metapopulation Models, explores how to connect multiple populations through a travel matrix. In the graph subsection, you'll find all you need to know about k-core Decomposition, while in the Visualization section, you can explore The Effects of Vaccination through a WSJ visualization.
In our regularly scheduled content, we dive into The Pragmatic Programmer for Machine Learning, learn how Andrey Markov & Claude Shannon Counted Letters to Build the First Language-Generation Models, How ‘Embeddings’ Encode What Words Mean, and exploreThe Math Behind Kernel Density Estimation.
On the academic front, we Train Language Models to Self-Correct via Reinforcement Learning, explore Complexity and entropy of natural patterns, and dive into a Primer on the Inner Workings of Transformer-based Language Models.
This week's book recommendation is "Why Machines Learn: The Elegant Math Behind Modern AI" by A. Ananthaswamy. You can find all the previous book recommendations on our website. In this week's video, we have a quick overview of How to Read Deep Learning Papers as a Software Engineer.
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Semper discentes,
The D4S Team
This week's book is "Why Machines Learn: The Elegant Math Behind Modern AI" by A. Ananthaswamy. The book introduces the main ideas and developments of Artificial Intelligence clearly and concisely. Starting with the invention of the Perceptron in the 50s, through all the significant developments of the last several decades, such as Support Vector Machines, Hopfield Networks, and Backpropagation, to the latest developments in Large Language Models. Ananthaswamy explains how they fit in the historical development of Computer Science and AI, as well as how they connect to insights originating in biology and psychology.
The book targets a general audience familiar with basic math. Mathematical concepts such as probability and linear algebra are introduced in an intuitive way that provides just enough detail to understand the more technical parts of the text. Overall, a great resource whether your reviewing these concepts or encountering them for the first time.
- The Pragmatic Programmer for Machine Learning [ppml.dev]
- Andrey Markov & Claude Shannon Counted Letters to Build the First Language-Generation Models [spectrum.ieee.org]
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How ‘Embeddings’ Encode What Words Mean — Sort Of [quantamagazine.org]
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Python in Excel – Available Now [techcommunity.microsoft.com]
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25 Amazing Python Tricks That Will Instantly Improve Your Code [medium.com/pythoneers]
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ICML 2024 Top Papers : What’s New in Machine Learning? [blog.cubed.run]
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The Math Behind Kernel Density Estimation [towardsdatascience.com]
- Complexity and entropy of natural patterns (H. Wang, C. Song, P. Gao)
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Replay shapes abstract cognitive maps for efficient social navigation (J.-Y. Son, M.-L. Vives, A. Bhandari, O. FeldmanHall)
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Durably reducing conspiracy beliefs through dialogues with AI (T. H. Costello, G. Pennycook, D. G. Rand)
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Training Language Models to Self-Correct via Reinforcement Learning (A. Kumar, V. Zhuang, R. Agarwal, Y. Su, J. D. Co-Reyes, A. Singh, K. Baumli, S. Iqbal, C. Bishop, R. Roelofs, L. M. Zhang, K. McKinney, D. Shrivastava, C. Paduraru, G. Tucker, D. Precup, F. Behbahani, A. Faust)
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Schrodinger's Memory: Large Language Models (W. Wang, Q. Li)
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A Primer on the Inner Workings of Transformer-based Language Models (J. Ferrando, G. Sarti, A. Bisazza, M. R. Costa-jussà)
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Chain of Thought Empowers Transformers to Solve Inherently Serial Problems (Z. Li, H. Liu, D. Zhou, T. Ma)
How to Read Deep Learning Papers as a Software Engineer
All the videos of the week are now available in our Youtube playlist.
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