Issue #185

December 12, 2022

This weeks Data Science Book is " Code: The hidden language of Computer Hardware and Software " by C. Petzold.This book is a bit of detour from our usual fare here at Data For Science as it focuses more on Computer Science than on Data Science per se . It provides a step-by-step timeline of how computers came to be, in a clear and concise way. It takes you on a tour of what happens "behind" the pixels on your screen, from logical gates on up, without requiring a heavy technical background. Chapter, by chapter, introduces each concept and technology necessary to make modern computers work. By the end of it, you'll have a detailed an intuitive understanding how Computers really work and will be able to mode easily optimize the way in which you write your own software. It might also make you want to learn to program in Assembly! A book that should be required reading for anyone interested in Computer Science.

Code: The hidden language of Computer Hardware and Software

1. Artificial intelligence is permeating business at last [economist.com]
2. Every modeler is supposed to be a great Python programmer [statmodeling.stat.columbia.edu]
3. A Data-Centric Introduction to Computing [dcic-world.org]
4. Fusion energy breakthrough by US scientists boosts clean power hopes [ft.com]
5. Why The Age Of American Progress Ended [theatlantic.com]
6. Modeling the Black-Scholes-Merton (BSM) Model in Python [python.plainenglish.io]
7. A/B Testing in Python with a User Average Metric [medium.com/@riddhimansherlekar]
8. Self-supervised learning: The dark matter of intelligence [ai.facebook.com]

• The Forward-Forward Algorithm: Some Preliminary Investigations (G. Hinton)
• Fragmentation of outage clusters during the recovery of power distribution grids (H. Wu, X. Meng, M. M. Danziger, S. P. Cornelius, H. Tian, A.-L. Barabási)
• Eigenvalue spectra and stability of directed complex networks (J. W. Baron)
• Discovering Latent Knowledge in Language Models Without Supervision (C. Burns, H. Ye, D. Klein, J. Steinhardt)
• Teaching Algorithmic Reasoning via In-context Learning (H. Zhou, A. Nova, H. Larochelle, A. Courville, B. Neyshabur, H. Sedghi)
• A Generalist Neural Algorithmic Learner (B. Ibarz, V. Kurin, G. Papamakarios, K. Nikiforou, M. Bennani, R. Csordás, A. Dudzik, M. Bošnjak, A. Vitvitskyi, Y. Rubanova, A. Deac, B. Bevilacqua, Y. Ganin, C. Blundell, P. Veličković)
• A Decade of Knowledge Graphs in Natural Language Processing: A Survey (P. Schneider, T. Schopf, J. Vladika, M. Galkin, E. Simperl, F. Matthes)

Billion Scale Deduplication using Approximate Nearest Neighbours

December 12, 2022

​